R Notebook: estimation of impact of arguments on gas costs; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_arguments approach.
PROGRAMS=pg_arguments_full5_c200_opc15x2 SAMPLESIZE=50 NSAMPLES=1`.
Expected a result file besu_pg_arguments_full5_c200_opc15x2_50_1.csv.
# the programs file is too large to be placed in github
programs = read.csv(paste("../../local/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, arg0, arg1, arg2, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)
")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count arg0 arg1 arg2 sample_id run_id measure_total_time_ns env
## 1 ADD 0 25 27 NA 0 0 3982569 besu
## 2 ADD 0 25 27 NA 0 1 5592032 besu
## 3 ADD 0 25 27 NA 0 2 8815079 besu
## 4 ADD 0 25 27 NA 0 3 5457919 besu
## 5 ADD 0 25 27 NA 0 4 5537141 besu
## 6 ADD 0 25 27 NA 0 5 5446211 besu
## program_id
## 1 ADD_0
## 2 ADD_0
## 3 ADD_0
## 4 ADD_0
## 5 ADD_0
## 6 ADD_0Remove outliers if needed.
# Extracts all OPCODEs from the `programs` data frame of the given arity (args taken off the stack).
extract_opcodes <- function(arity) {
if (!missing(arity)) {
if (arity == 0) {
programs = programs[which(is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 1) {
programs = programs[which(!is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 2) {
programs = programs[which(!is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 3) {
programs = programs[which(!is.na(programs$arg2)), ]
}
}
unique(programs$opcode)
}if ( (!removed_outliers) && (!removed_outliers_2)) {
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
}
if (removed_outliers) {
par(mfrow=c(length(all_envs)*2, 1))
# before
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
measurements = remove_outliers(measurements, 'measure_total_time_ns', FALSE)
# after
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'no_outliers'))
}all_opcodes = extract_opcodes()
nullary_opcodes = extract_opcodes(0)
unary_opcodes = extract_opcodes(1)
binary_opcodes = extract_opcodes(2)
ternary_opcodes = extract_opcodes(3)
div_opcodes = c('DIV', 'MOD', 'SDIV', 'SMOD')measurements$expensive = NA
measurements[which(measurements$opcode %in% div_opcodes), ]$expensive =
measurements[which(measurements$opcode %in% div_opcodes), ]$arg0 >
measurements[which(measurements$opcode %in% div_opcodes), ]$arg1
# remember that argX is the byte-size of the argument in these measurements
measurements[which(measurements$opcode == 'ADDMOD'), ]$expensive =
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg0 +
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg1 >
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg2
measurements[which(measurements$opcode == 'MULMOD'), ]$expensive =
measurements[which(measurements$opcode == 'MULMOD'), ]$arg0 +
measurements[which(measurements$opcode == 'MULMOD'), ]$arg1 >
measurements[which(measurements$opcode == 'MULMOD'), ]$arg2if (removed_outliers_2) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}Detailed view
This is massive and detailed overview on the impact of arguments.
Because of the number of charts, only op count = 30 is
eligible. Feel free to change it, but that should not be anyhow more
informative. The visualizations do not guarantee that all dependencies
are clearly seen. Especially for binary and ternary opcodes where
impacts of arg0, arg1 and arg2 are mixed. But if a dependency is
graphically noticeable that you should expect also statistical
dependency.
for (env in all_envs) {
for (opcode in unary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
}
for (opcode in binary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg1', 'opcount 0'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg1', 'opcount 15'))
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg1', 'opcount 30'))
}
for (opcode in ternary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg1', 'opcount 0'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg1', 'opcount 15'))
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg1', 'opcount 30'))
# plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg2', 'opcount 0'))
# plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg2', 'opcount 15'))
plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg2', 'opcount 30'))
}
}
Models
Notes: 1. Outliers need to be removed if detected 2. The
argX:op_count interactions measure the impact on the OPCODE
3. The argX are just auxiliary variables added to exclude
the effect of cheaper/more expensive PUSHes. We only want to extract the
effect of the argument on the measured OPCODE repeated
op_count times.
# Every `arg` coefficient represents the impact of the argument's byte size growing by 1.
# We treat as impactful the arguments where p-value is effectively zero. The previous approach was:
# Treat as impactful the arguments, where:
# 1. The estimate is significant with confidence 0.001
# 2. The increase of arg's byte size by 1 will increase the cost by more than 1%
# but it turned out to be much less stable in practice.
p_value_thresh = 1e-30
# p_value_thresh = 0.001
impact_ratio = 0.00
# impact_ratio = 0.01
arg_lm <- function(df, opcode, env, formula) {
data = df[which(df$opcode==opcode & df$env==env), ]
lm(formula, data=data)
}
# Adds the results from the estimated `model` to the `results_df` data frame.
# You need to provide the corresponding `opcode`, `env` and `arity`.
# `results_df` is assumed to have the columns as the `first_pass` data frame has (see below)
add_arg_results <- function(model, opcode, env, results_df, arity) {
stopifnot(arity > 0)
all_coefficients = summary(model)$coefficients
arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
pure_op_count_coeff = all_coefficients["op_count", 1]
# will be filled if any is impacting
args_ns = c(NA, NA, NA)
# will be always if arg present
args_ns_raw = c(NA, NA, NA)
args_ns_p = c(NA, NA, NA)
if (arity == 1) {
# there's only one arg coefficient here, silly R forces us to take a special case path...
has_significant = arg_coefficients[4] < p_value_thresh
if (has_significant) {
coefficient_impact = abs(arg_coefficients[1])
has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
} else {
has_impacting = FALSE
}
if (has_impacting) {
args_ns[1] = arg_coefficients[1]
}
args_ns_raw[1] = arg_coefficients[1]
args_ns_p[1] = arg_coefficients[4]
} else {
significant = arg_coefficients[, 4] < p_value_thresh
has_significant = length(which(significant)) > 0
coefficient_impact = abs(arg_coefficients[, 1])
can_impact = significant & coefficient_impact > pure_op_count_coeff * impact_ratio
has_impacting = length(which(can_impact)) > 0
args_ns[which(can_impact)] = arg_coefficients[which(can_impact), 1]
args_ns_raw[1:arity] = arg_coefficients[1:arity, 1]
args_ns_p[1:arity] = arg_coefficients[1:arity, 4]
}
# NAs for the "expensive" arg columns. See above for the columns layout
results_df[nrow(results_df) + 1, ] = c(opcode, env, has_significant, has_impacting, pure_op_count_coeff, args_ns, NA, args_ns_raw, NA, args_ns_p, NA)
return(results_df)
}
# Adds the results from the estimated `model` to the `results_df` data frame, where the model is
# specifically the one gauged towards the "division" OPCODEs like `DIV`.
# See also `add_arg_results`
add_arg_expensive_results <- function(model, opcode, env, results_df, arity) {
stopifnot(arity > 0)
all_coefficients = summary(model)$coefficients
pure_op_count_coeff = all_coefficients["op_count", 1]
expensive = NA
# there's only one arg coefficient here, silly R forces us to take a special case path...
has_significant = all_coefficients['op_count:expensiveTRUE', 4] < p_value_thresh
if (has_significant) {
coefficient_impact = abs(all_coefficients['op_count:expensiveTRUE', 1])
has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
} else {
has_impacting = FALSE
}
if (has_impacting) {
expensive = all_coefficients['op_count:expensiveTRUE', 1]
}
expensive_raw = all_coefficients['op_count:expensiveTRUE', 1]
expensive_p = all_coefficients['op_count:expensiveTRUE', 4]
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns'] = expensive
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_raw'] = expensive_raw
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_p'] = expensive_p
return(results_df)
}
# Goes through all the families of OPCODEs and fits and displays their respective `measure_arguments`
# models.
# Results are gathered in a common `results_df` data frame.
analyze_for_env <- function(df, results_df, env) {
for (opcode in unary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg0:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 1)
}
for (opcode in binary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 2)
}
for (opcode in ternary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + arg0:op_count + arg1:op_count + arg2:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 3)
}
for (opcode in div_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + expensive:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_expensive_results(model, opcode, env, results_df, 2)
}
for (opcode in c('ADDMOD', 'MULMOD')) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + expensive:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_expensive_results(model, opcode, env, results_df, 3)
}
return(results_df)
}This is the so-called “first-pass” at the estimation procedure, where
we estimated all possible argument impact variables for all OPCODEs. We
gather all the results in the first_pass table, inspect
this to see where the arguments turned out to be significantly impacting
the computation cost.
first_pass = data.frame(matrix(ncol = 17, nrow = 0))
colnames(first_pass) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns',
'arg0_ns_raw', 'arg1_ns_raw', 'arg2_ns_raw', 'expensive_ns_raw',
'arg0_ns_p', 'arg1_ns_p', 'arg2_ns_p', 'expensive_ns_p')
first_pass = analyze_for_env(measurements, first_pass, env)## [1] "ISZERO" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1060457 -847419 -728366 995771 14083973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4132643.82 22620.57 182.694 <0.0000000000000002 ***
## op_count 31999.46 1168.12 27.394 <0.0000000000000002 ***
## arg0 682.32 1265.21 0.539 0.590
## op_count:arg0 -40.82 65.34 -0.625 0.532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1248000 on 29996 degrees of freedom
## Multiple R-squared: 0.08656, Adjusted R-squared: 0.08647
## F-statistic: 947.5 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "NOT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1107185 -863900 -736841 1125167 12084693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4141164.99 26148.33 158.372 <0.0000000000000002 ***
## op_count 35303.23 1350.29 26.145 <0.0000000000000002 ***
## arg0 -82.08 1346.81 -0.061 0.951
## op_count:arg0 -24.29 69.55 -0.349 0.727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1343000 on 29996 degrees of freedom
## Multiple R-squared: 0.09198, Adjusted R-squared: 0.09189
## F-statistic: 1013 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATALOAD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1519415 -1273183 -1205196 -1047283 27989872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6445387.0724 54652.5448 117.934 <0.0000000000000002 ***
## op_count 58894.5102 2822.2453 20.868 <0.0000000000000002 ***
## arg0 2.1622 5.4544 0.396 0.692
## op_count:arg0 0.1082 0.2817 0.384 0.701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2872000 on 29996 degrees of freedom
## Multiple R-squared: 0.0612, Adjusted R-squared: 0.06111
## F-statistic: 651.8 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "POP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1003736 -567559 -474536 598786 5523553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2314221.38 15615.19 148.203 < 0.0000000000000002 ***
## op_count 23567.67 806.36 29.227 < 0.0000000000000002 ***
## arg0 5041.70 816.08 6.178 0.000000000658 ***
## op_count:arg0 -142.34 42.14 -3.378 0.000732 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 837400 on 29996 degrees of freedom
## Multiple R-squared: 0.08886, Adjusted R-squared: 0.08877
## F-statistic: 975.2 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "MLOAD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1387736 -1185657 -1129417 -949333 22813147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6397229.8138 53553.2921 119.455 <0.0000000000000002 ***
## op_count 26236.4589 2765.4801 9.487 <0.0000000000000002 ***
## arg0 0.5826 5.2323 0.111 0.911
## op_count:arg0 -0.1532 0.2702 -0.567 0.571
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2725000 on 29996 degrees of freedom
## Multiple R-squared: 0.01234, Adjusted R-squared: 0.01224
## F-statistic: 124.9 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPI" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -916939 -754302 -704140 890577 11450723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4073354.04 20021.22 203.452 <0.0000000000000002 ***
## op_count 19246.02 1033.89 18.615 <0.0000000000000002 ***
## arg0 591.77 1079.27 0.548 0.583
## op_count:arg0 -11.77 55.73 -0.211 0.833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1122000 on 29996 degrees of freedom
## Multiple R-squared: 0.04151, Adjusted R-squared: 0.04141
## F-statistic: 433 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP1" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -831138 -712741 -665680 799763 6836287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4121194.79 20456.61 201.460 <0.0000000000000002 ***
## op_count 10321.24 1056.37 9.770 <0.0000000000000002 ***
## arg0 236.53 1059.40 0.223 0.823
## op_count:arg0 -37.27 54.71 -0.681 0.496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1069000 on 29996 degrees of freedom
## Multiple R-squared: 0.01219, Adjusted R-squared: 0.01209
## F-statistic: 123.4 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP2" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -834486 -709984 -665000 803518 14766076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4134314.98 18924.12 218.468 <0.0000000000000002 ***
## op_count 9695.05 977.24 9.921 <0.0000000000000002 ***
## arg0 -934.83 1004.98 -0.930 0.352
## op_count:arg0 22.11 51.90 0.426 0.670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1061000 on 29996 degrees of freedom
## Multiple R-squared: 0.01331, Adjusted R-squared: 0.01321
## F-statistic: 134.9 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP3" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -836674 -716168 -670148 791802 6627959
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4137820.05 20428.21 202.554 <0.0000000000000002 ***
## op_count 9524.06 1054.91 9.028 <0.0000000000000002 ***
## arg0 -659.03 1111.32 -0.593 0.553
## op_count:arg0 19.14 57.39 0.334 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1085000 on 29996 degrees of freedom
## Multiple R-squared: 0.01217, Adjusted R-squared: 0.01207
## F-statistic: 123.2 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP4" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -841897 -721093 -674366 791160 9671516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4146867.81 20163.27 205.664 <0.0000000000000002 ***
## op_count 8979.22 1041.23 8.624 <0.0000000000000002 ***
## arg0 -568.07 1064.41 -0.534 0.594
## op_count:arg0 28.84 54.97 0.525 0.600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1090000 on 29996 degrees of freedom
## Multiple R-squared: 0.01117, Adjusted R-squared: 0.01107
## F-statistic: 112.9 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP5" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -828700 -709724 -662940 803383 6700240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4128791.00 19388.24 212.953 <0.0000000000000002 ***
## op_count 10060.78 1001.20 10.049 <0.0000000000000002 ***
## arg0 -399.26 1007.73 -0.396 0.692
## op_count:arg0 -16.94 52.04 -0.325 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1060000 on 29996 degrees of freedom
## Multiple R-squared: 0.01263, Adjusted R-squared: 0.01254
## F-statistic: 127.9 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP6" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -831530 -716514 -670310 798397 6700253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4119078.61 21212.38 194.183 <0.0000000000000002 ***
## op_count 10187.46 1095.40 9.300 <0.0000000000000002 ***
## arg0 233.68 1102.01 0.212 0.832
## op_count:arg0 -16.20 56.91 -0.285 0.776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075000 on 29996 degrees of freedom
## Multiple R-squared: 0.01259, Adjusted R-squared: 0.01249
## F-statistic: 127.5 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP7" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -837408 -719899 -672398 804233 6873987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4162202.82 20444.19 203.589 <0.0000000000000002 ***
## op_count 9391.47 1055.73 8.896 <0.0000000000000002 ***
## arg0 -227.34 1095.83 -0.207 0.836
## op_count:arg0 11.26 56.59 0.199 0.842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1068000 on 29996 degrees of freedom
## Multiple R-squared: 0.01193, Adjusted R-squared: 0.01183
## F-statistic: 120.7 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -865412 -734168 -686372 801428 7207796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4186065.38 19728.84 212.180 <0.0000000000000002 ***
## op_count 9890.84 1018.79 9.708 <0.0000000000000002 ***
## arg0 -267.88 1123.45 -0.238 0.812
## op_count:arg0 37.77 58.01 0.651 0.515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1096000 on 29996 degrees of freedom
## Multiple R-squared: 0.0135, Adjusted R-squared: 0.01341
## F-statistic: 136.9 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP9" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -844309 -720682 -674027 794711 13099809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4105969.26 20556.09 199.745 <0.0000000000000002 ***
## op_count 11202.93 1061.51 10.554 <0.0000000000000002 ***
## arg0 1341.01 1120.43 1.197 0.231
## op_count:arg0 -71.46 57.86 -1.235 0.217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1089000 on 29996 degrees of freedom
## Multiple R-squared: 0.01269, Adjusted R-squared: 0.01259
## F-statistic: 128.5 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP10" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -830714 -714547 -668248 796378 6301492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4129045.30 20600.25 200.437 <0.0000000000000002 ***
## op_count 9059.65 1063.79 8.516 <0.0000000000000002 ***
## arg0 -279.99 1027.37 -0.273 0.785
## op_count:arg0 53.00 53.05 0.999 0.318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075000 on 29996 degrees of freedom
## Multiple R-squared: 0.01285, Adjusted R-squared: 0.01275
## F-statistic: 130.2 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP11" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -875129 -744075 -692387 810016 6846893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4221536.23 21004.84 200.979 <0.0000000000000002 ***
## op_count 10914.53 1084.69 10.062 <0.0000000000000002 ***
## arg0 471.18 1098.98 0.429 0.668
## op_count:arg0 -38.96 56.75 -0.687 0.492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1088000 on 29996 degrees of freedom
## Multiple R-squared: 0.01317, Adjusted R-squared: 0.01307
## F-statistic: 133.4 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP12" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -843361 -721669 -674859 791035 16949621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4128958.77 19876.28 207.733 <0.0000000000000002 ***
## op_count 9817.15 1026.41 9.565 <0.0000000000000002 ***
## arg0 551.72 1054.53 0.523 0.601
## op_count:arg0 -17.65 54.46 -0.324 0.746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1092000 on 29996 degrees of freedom
## Multiple R-squared: 0.0113, Adjusted R-squared: 0.0112
## F-statistic: 114.3 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP13" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -845742 -719683 -672757 803951 11132078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4163493.16 20759.14 200.562 <0.0000000000000002 ***
## op_count 8887.76 1072.00 8.291 <0.0000000000000002 ***
## arg0 -305.92 1064.98 -0.287 0.774
## op_count:arg0 36.56 55.00 0.665 0.506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1060000 on 29996 degrees of freedom
## Multiple R-squared: 0.01197, Adjusted R-squared: 0.01187
## F-statistic: 121.1 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP14" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -844987 -722990 -675816 803002 16302795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4154979.90 20047.44 207.257 <0.0000000000000002 ***
## op_count 9671.91 1035.25 9.343 <0.0000000000000002 ***
## arg0 -77.37 1079.83 -0.072 0.943
## op_count:arg0 14.08 55.76 0.253 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1079000 on 29996 degrees of freedom
## Multiple R-squared: 0.01247, Adjusted R-squared: 0.01238
## F-statistic: 126.3 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP15" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -835286 -713295 -666919 799078 9016056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4123734.765 22060.000 186.933 <0.0000000000000002 ***
## op_count 10058.845 1139.174 8.830 <0.0000000000000002 ***
## arg0 -96.024 1141.306 -0.084 0.933
## op_count:arg0 -0.898 58.937 -0.015 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1073000 on 29996 degrees of freedom
## Multiple R-squared: 0.01298, Adjusted R-squared: 0.01288
## F-statistic: 131.5 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DUP16" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -911101 -770606 -713074 813799 12491745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4310094.16 21428.76 201.136 <0.0000000000000002 ***
## op_count 11105.95 1106.58 10.036 <0.0000000000000002 ***
## arg0 160.55 1118.63 0.144 0.886
## op_count:arg0 -33.66 57.77 -0.583 0.560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1129000 on 29996 degrees of freedom
## Multiple R-squared: 0.01293, Adjusted R-squared: 0.01283
## F-statistic: 130.9 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "ADD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1452551 -971335 -772328 861188 17081974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4141012.16 40336.52 102.662 <0.0000000000000002 ***
## op_count 59139.74 2082.97 28.392 <0.0000000000000002 ***
## arg0 1286.96 1589.48 0.810 0.418
## arg1 -115.45 1553.61 -0.074 0.941
## op_count:arg0 995.35 82.08 12.126 <0.0000000000000002 ***
## op_count:arg1 1029.12 80.23 12.827 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1569000 on 29994 degrees of freedom
## Multiple R-squared: 0.3587, Adjusted R-squared: 0.3586
## F-statistic: 3356 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MUL" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1333931 -968576 -749667 822688 18338302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4120930.48 40757.93 101.107 <0.0000000000000002 ***
## op_count 92060.50 2104.73 43.740 <0.0000000000000002 ***
## arg0 300.45 1611.41 0.186 0.852
## arg1 740.15 1614.92 0.458 0.647
## op_count:arg0 -40.53 83.21 -0.487 0.626
## op_count:arg1 -58.86 83.39 -0.706 0.480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1581000 on 29994 degrees of freedom
## Multiple R-squared: 0.3296, Adjusted R-squared: 0.3295
## F-statistic: 2949 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SUB" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1245478 -941385 -762172 883249 9947946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4138407.404 38685.317 106.976 <0.0000000000000002 ***
## op_count 58148.108 1997.701 29.108 <0.0000000000000002 ***
## arg0 714.573 1526.006 0.468 0.640
## arg1 241.855 1518.447 0.159 0.873
## op_count:arg0 -69.634 78.803 -0.884 0.377
## op_count:arg1 4.659 78.412 0.059 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1491000 on 29994 degrees of freedom
## Multiple R-squared: 0.1803, Adjusted R-squared: 0.1801
## F-statistic: 1319 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2405845 -907432 -710704 813907 15550762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4167055.49 41184.45 101.180 <0.0000000000000002 ***
## op_count 64440.81 2126.76 30.300 <0.0000000000000002 ***
## arg0 638.58 1648.54 0.387 0.698
## arg1 -1170.95 1611.29 -0.727 0.467
## op_count:arg0 3280.95 85.13 38.540 <0.0000000000000002 ***
## op_count:arg1 -886.72 83.21 -10.657 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1659000 on 29994 degrees of freedom
## Multiple R-squared: 0.4203, Adjusted R-squared: 0.4202
## F-statistic: 4350 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3174675 -1094038 -818233 582064 18761620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4243653.4 53764.6 78.930 <0.0000000000000002 ***
## op_count 117636.6 2776.4 42.370 <0.0000000000000002 ***
## arg0 820.1 2186.7 0.375 0.708
## arg1 118.6 2197.9 0.054 0.957
## op_count:arg0 3813.5 112.9 33.771 <0.0000000000000002 ***
## op_count:arg1 -1002.9 113.5 -8.836 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2216000 on 29994 degrees of freedom
## Multiple R-squared: 0.4829, Adjusted R-squared: 0.4828
## F-statistic: 5602 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3686767 -1039670 -793017 583610 22307878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4262321.3 55641.6 76.603 < 0.0000000000000002 ***
## op_count 106716.4 2873.3 37.140 < 0.0000000000000002 ***
## arg0 1832.5 2179.9 0.841 0.401
## arg1 -1577.9 2198.9 -0.718 0.473
## op_count:arg0 4043.6 112.6 35.922 < 0.0000000000000002 ***
## op_count:arg1 -612.9 113.6 -5.397 0.0000000682 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2212000 on 29994 degrees of freedom
## Multiple R-squared: 0.4935, Adjusted R-squared: 0.4934
## F-statistic: 5845 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3570383 -1055564 -810531 594719 21869282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4272575.5 55135.1 77.493 < 0.0000000000000002 ***
## op_count 107143.6 2847.2 37.632 < 0.0000000000000002 ***
## arg0 329.8 2118.7 0.156 0.876
## arg1 -828.4 2309.5 -0.359 0.720
## op_count:arg0 4144.2 109.4 37.878 < 0.0000000000000002 ***
## op_count:arg1 -622.3 119.3 -5.218 0.000000182 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2198000 on 29994 degrees of freedom
## Multiple R-squared: 0.4882, Adjusted R-squared: 0.4881
## F-statistic: 5722 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "EXP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -147840350 -29634522 -807198 5925872 234791770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4282477 1394834 3.070 0.00214 **
## op_count -65006 72029 -0.902 0.36680
## arg0 -1452 59364 -0.024 0.98049
## arg1 -1514 57302 -0.026 0.97891
## op_count:arg0 27402 3066 8.939 < 0.0000000000000002 ***
## op_count:arg1 161222 2959 54.484 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 61210000 on 29994 degrees of freedom
## Multiple R-squared: 0.3828, Adjusted R-squared: 0.3827
## F-statistic: 3721 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SIGNEXTEND" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1311586 -970724 -759764 821274 16474463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4148547.139 37895.804 109.472 <0.0000000000000002 ***
## op_count 146350.280 1956.931 74.786 <0.0000000000000002 ***
## arg0 -1.469 1524.030 -0.001 0.999
## arg1 -135.033 1572.033 -0.086 0.932
## op_count:arg0 -22.153 78.701 -0.281 0.778
## op_count:arg1 -20.448 81.179 -0.252 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1564000 on 29994 degrees of freedom
## Multiple R-squared: 0.5656, Adjusted R-squared: 0.5655
## F-statistic: 7809 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "LT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1180063 -906631 -750395 1018892 11134871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4132145.01 35910.27 115.069 <0.0000000000000002 ***
## op_count 55058.75 1854.40 29.691 <0.0000000000000002 ***
## arg0 1093.68 1417.68 0.771 0.440
## arg1 -279.39 1458.26 -0.192 0.848
## op_count:arg0 -103.76 73.21 -1.417 0.156
## op_count:arg1 -28.15 75.30 -0.374 0.709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1399000 on 29994 degrees of freedom
## Multiple R-squared: 0.1768, Adjusted R-squared: 0.1766
## F-statistic: 1288 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "GT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1197805 -916813 -756633 1016013 8736174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4154932.45 35580.53 116.775 <0.0000000000000002 ***
## op_count 53809.41 1837.37 29.286 <0.0000000000000002 ***
## arg0 -739.19 1494.58 -0.495 0.621
## arg1 726.71 1446.54 0.502 0.615
## op_count:arg0 24.55 77.18 0.318 0.750
## op_count:arg1 -57.58 74.70 -0.771 0.441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1427000 on 29994 degrees of freedom
## Multiple R-squared: 0.1728, Adjusted R-squared: 0.1727
## F-statistic: 1253 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SLT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1167875 -863149 -734138 1099464 11260398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4129074.26 35835.98 115.221 <0.0000000000000002 ***
## op_count 43845.61 1850.56 23.693 <0.0000000000000002 ***
## arg0 639.29 1342.97 0.476 0.634
## arg1 -22.96 1293.67 -0.018 0.986
## op_count:arg0 654.23 69.35 9.434 <0.0000000000000002 ***
## op_count:arg1 746.94 66.80 11.181 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1333000 on 29994 degrees of freedom
## Multiple R-squared: 0.2876, Adjusted R-squared: 0.2875
## F-statistic: 2422 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SGT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1170274 -872378 -731210 1103054 15589137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4120257.05 33831.54 121.787 <0.0000000000000002 ***
## op_count 43797.41 1747.05 25.069 <0.0000000000000002 ***
## arg0 291.62 1442.04 0.202 0.840
## arg1 561.18 1304.62 0.430 0.667
## op_count:arg0 696.03 74.47 9.347 <0.0000000000000002 ***
## op_count:arg1 723.25 67.37 10.735 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1337000 on 29994 degrees of freedom
## Multiple R-squared: 0.2823, Adjusted R-squared: 0.2821
## F-statistic: 2359 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "EQ" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1208793 -909296 -753908 1013924 18396553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4146156.55 34852.13 118.964 <0.0000000000000002 ***
## op_count 53196.24 1799.76 29.557 <0.0000000000000002 ***
## arg0 755.33 1389.58 0.544 0.587
## arg1 -389.53 1459.14 -0.267 0.790
## op_count:arg0 -68.38 71.76 -0.953 0.341
## op_count:arg1 -14.27 75.35 -0.189 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1418000 on 29994 degrees of freedom
## Multiple R-squared: 0.1674, Adjusted R-squared: 0.1673
## F-statistic: 1206 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "AND" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1233689 -937149 -751375 890441 12363117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4127451.23 36026.89 114.566 <0.0000000000000002 ***
## op_count 57094.48 1860.42 30.689 <0.0000000000000002 ***
## arg0 80.21 1427.16 0.056 0.955
## arg1 915.59 1551.61 0.590 0.555
## op_count:arg0 -10.77 73.70 -0.146 0.884
## op_count:arg1 -55.36 80.13 -0.691 0.490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1489000 on 29994 degrees of freedom
## Multiple R-squared: 0.175, Adjusted R-squared: 0.1748
## F-statistic: 1272 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "OR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1238620 -944064 -768957 895445 18273658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4160905.18 37291.85 111.577 <0.0000000000000002 ***
## op_count 55973.95 1925.74 29.066 <0.0000000000000002 ***
## arg0 -304.64 1503.49 -0.203 0.839
## arg1 520.70 1501.03 0.347 0.729
## op_count:arg0 12.00 77.64 0.155 0.877
## op_count:arg1 -62.06 77.51 -0.801 0.423
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1492000 on 29994 degrees of freedom
## Multiple R-squared: 0.1702, Adjusted R-squared: 0.17
## F-statistic: 1230 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "XOR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1241768 -939288 -755388 876842 9928134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4144817.48 36156.82 114.634 <0.0000000000000002 ***
## op_count 56799.39 1867.13 30.421 <0.0000000000000002 ***
## arg0 -118.81 1435.37 -0.083 0.934
## arg1 396.28 1450.50 0.273 0.785
## op_count:arg0 -11.17 74.12 -0.151 0.880
## op_count:arg1 -50.82 74.90 -0.679 0.497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1492000 on 29994 degrees of freedom
## Multiple R-squared: 0.1732, Adjusted R-squared: 0.1731
## F-statistic: 1257 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "BYTE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1222118 -917128 -749720 1086687 9992078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4145068.99 36882.81 112.385 <0.0000000000000002 ***
## op_count 52660.87 1904.62 27.649 <0.0000000000000002 ***
## arg0 -442.59 1461.44 -0.303 0.762
## arg1 719.36 1434.63 0.501 0.616
## op_count:arg0 -79.43 75.47 -1.052 0.293
## op_count:arg1 47.30 74.08 0.639 0.523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1416000 on 29994 degrees of freedom
## Multiple R-squared: 0.169, Adjusted R-squared: 0.1689
## F-statistic: 1220 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SHL" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1027549 -750902 -681411 844822 10995282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4155756.77 27337.96 152.014 < 0.0000000000000002 ***
## op_count 28189.52 1411.73 19.968 < 0.0000000000000002 ***
## arg0 -1492.03 1079.01 -1.383 0.167
## arg1 83.91 1121.58 0.075 0.940
## op_count:arg0 -397.66 55.72 -7.137 0.000000000000977 ***
## op_count:arg1 24.22 57.92 0.418 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1114000 on 29994 degrees of freedom
## Multiple R-squared: 0.06032, Adjusted R-squared: 0.06016
## F-statistic: 385.1 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SHR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1103800 -760369 -681157 833417 10130159
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4126062.09 25916.16 159.208 <0.0000000000000002 ***
## op_count 30249.45 1338.30 22.603 <0.0000000000000002 ***
## arg0 -542.70 1133.03 -0.479 0.632
## arg1 1120.39 1113.21 1.006 0.314
## op_count:arg0 -525.46 58.51 -8.981 <0.0000000000000002 ***
## op_count:arg1 42.21 57.49 0.734 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1131000 on 29994 degrees of freedom
## Multiple R-squared: 0.06643, Adjusted R-squared: 0.06627
## F-statistic: 426.9 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SAR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -937537 -752382 -691909 851007 7366924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4143583.37 29897.95 138.591 < 0.0000000000000002 ***
## op_count 25204.25 1543.92 16.325 < 0.0000000000000002 ***
## arg0 -79.67 1141.96 -0.070 0.944
## arg1 -311.85 1127.83 -0.277 0.782
## op_count:arg0 -245.13 58.97 -4.157 0.0000324 ***
## op_count:arg1 38.04 58.24 0.653 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1109000 on 29994 degrees of freedom
## Multiple R-squared: 0.05733, Adjusted R-squared: 0.05718
## F-statistic: 364.9 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1621512 -1306367 -1121743 -919516 15268672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5017448.0931412 68426.8094813 73.326 <0.0000000000000002 ***
## op_count 33656.2601600 3533.5452474 9.525 <0.0000000000000002 ***
## arg0 3.0170568 5.3037275 0.569 0.569
## arg1 0.5141174 5.1909987 0.099 0.921
## op_count:arg0 0.0131320 0.2738833 0.048 0.962
## op_count:arg1 0.0003833 0.2680620 0.001 0.999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2743000 on 29994 degrees of freedom
## Multiple R-squared: 0.02226, Adjusted R-squared: 0.0221
## F-statistic: 136.6 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1633584 -1306479 -1219350 -884711 18758774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4983659.55298 63581.77601 78.382 < 0.0000000000000002 ***
## op_count 23946.91848 3283.34879 7.293 0.00000000000031 ***
## arg0 3.81809 5.33410 0.716 0.474
## arg1 -3.57017 5.35970 -0.666 0.505
## op_count:arg0 -0.05608 0.27545 -0.204 0.839
## op_count:arg1 0.18133 0.27677 0.655 0.512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2687000 on 29994 degrees of freedom
## Multiple R-squared: 0.01282, Adjusted R-squared: 0.01265
## F-statistic: 77.88 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP1" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1009182 -562743 -467910 616471 9783102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2293012.2426 20885.3727 109.790 < 0.0000000000000002 ***
## op_count 28328.3995 1078.5160 26.266 < 0.0000000000000002 ***
## arg0 5140.3895 835.7878 6.150 0.000000000783 ***
## arg1 831.2802 813.7339 1.022 0.307
## op_count:arg0 -170.5966 43.1599 -3.953 0.000077463012 ***
## op_count:arg1 0.9837 42.0210 0.023 0.981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 837100 on 29994 degrees of freedom
## Multiple R-squared: 0.125, Adjusted R-squared: 0.1248
## F-statistic: 856.6 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP2" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1019310 -574359 -477209 608586 9138663
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2304964.80 21886.83 105.313 < 0.0000000000000002 ***
## op_count 28314.66 1130.23 25.052 < 0.0000000000000002 ***
## arg0 1174.37 891.81 1.317 0.18790
## arg1 4464.84 843.84 5.291 0.000000122 ***
## op_count:arg0 -38.59 46.05 -0.838 0.40205
## op_count:arg1 -123.54 43.58 -2.835 0.00459 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 860600 on 29994 degrees of freedom
## Multiple R-squared: 0.118, Adjusted R-squared: 0.1179
## F-statistic: 802.7 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP3" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -974557 -570412 -476034 606228 7326450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2310447.933 20650.674 111.882 < 0.0000000000000002 ***
## op_count 28164.916 1066.396 26.411 < 0.0000000000000002 ***
## arg0 3428.964 829.305 4.135 0.0000356 ***
## arg1 580.094 808.802 0.717 0.47324
## op_count:arg0 -118.101 42.825 -2.758 0.00582 **
## op_count:arg1 5.876 41.766 0.141 0.88813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 843600 on 29994 degrees of freedom
## Multiple R-squared: 0.1283, Adjusted R-squared: 0.1282
## F-statistic: 883.3 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP4" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1001165 -579535 -482270 598954 6618157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2435999.88 21684.62 112.338 <0.0000000000000002 ***
## op_count 24767.03 1119.79 22.118 <0.0000000000000002 ***
## arg0 -512.89 886.37 -0.579 0.5628
## arg1 -1875.12 846.03 -2.216 0.0267 *
## op_count:arg0 28.98 45.77 0.633 0.5266
## op_count:arg1 42.01 43.69 0.962 0.3363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 859500 on 29994 degrees of freedom
## Multiple R-squared: 0.1204, Adjusted R-squared: 0.1202
## F-statistic: 820.9 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP5" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -997432 -573380 -481983 608323 13518922
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2380278.43 22703.00 104.844 <0.0000000000000002 ***
## op_count 27311.44 1172.38 23.296 <0.0000000000000002 ***
## arg0 883.98 844.42 1.047 0.295
## arg1 10.26 908.43 0.011 0.991
## op_count:arg0 -57.02 43.61 -1.308 0.191
## op_count:arg1 -29.96 46.91 -0.639 0.523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 854800 on 29994 degrees of freedom
## Multiple R-squared: 0.121, Adjusted R-squared: 0.1208
## F-statistic: 825.7 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP6" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1002151 -577676 -488135 617848 7806843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2426775.09 22783.93 106.513 <0.0000000000000002 ***
## op_count 26300.78 1176.56 22.354 <0.0000000000000002 ***
## arg0 -1438.60 866.37 -1.660 0.0968 .
## arg1 758.09 855.85 0.886 0.3757
## op_count:arg0 22.93 44.74 0.512 0.6083
## op_count:arg1 -47.78 44.20 -1.081 0.2797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 864200 on 29994 degrees of freedom
## Multiple R-squared: 0.1187, Adjusted R-squared: 0.1186
## F-statistic: 808.3 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP7" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1011746 -585850 -491707 632744 7016892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2451892.17 22076.87 111.062 <0.0000000000000002 ***
## op_count 25855.34 1140.04 22.679 <0.0000000000000002 ***
## arg0 645.64 892.60 0.723 0.469
## arg1 -643.95 917.91 -0.702 0.483
## op_count:arg0 -60.26 46.09 -1.307 0.191
## op_count:arg1 40.34 47.40 0.851 0.395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 863700 on 29994 degrees of freedom
## Multiple R-squared: 0.1162, Adjusted R-squared: 0.116
## F-statistic: 788.4 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -986413 -567592 -474544 608266 5449407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2387520.53 20350.66 117.319 <0.0000000000000002 ***
## op_count 25837.19 1050.90 24.586 <0.0000000000000002 ***
## arg0 -749.87 839.38 -0.893 0.372
## arg1 400.47 857.20 0.467 0.640
## op_count:arg0 47.68 43.35 1.100 0.271
## op_count:arg1 -30.00 44.27 -0.678 0.498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 848500 on 29994 degrees of freedom
## Multiple R-squared: 0.1246, Adjusted R-squared: 0.1244
## F-statistic: 853.5 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP9" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1003402 -579136 -487734 595872 15161056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2432922.00 23609.97 103.046 <0.0000000000000002 ***
## op_count 25717.12 1219.21 21.093 <0.0000000000000002 ***
## arg0 -605.54 917.43 -0.660 0.509
## arg1 -1053.24 872.02 -1.208 0.227
## op_count:arg0 26.29 47.38 0.555 0.579
## op_count:arg1 -11.73 45.03 -0.260 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 872200 on 29994 degrees of freedom
## Multiple R-squared: 0.1174, Adjusted R-squared: 0.1173
## F-statistic: 798.1 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP10" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1027478 -592864 -505214 638552 5225823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2473927.63 23204.93 106.612 <0.0000000000000002 ***
## op_count 27072.03 1198.30 22.592 <0.0000000000000002 ***
## arg0 578.15 846.39 0.683 0.495
## arg1 -1117.14 926.00 -1.206 0.228
## op_count:arg0 -11.41 43.71 -0.261 0.794
## op_count:arg1 2.97 47.82 0.062 0.950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 877100 on 29994 degrees of freedom
## Multiple R-squared: 0.124, Adjusted R-squared: 0.1239
## F-statistic: 849.3 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP11" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1026664 -573809 -471586 603506 5348366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2378012.84 20658.81 115.109 <0.0000000000000002 ***
## op_count 26497.64 1066.82 24.838 <0.0000000000000002 ***
## arg0 1838.94 864.71 2.127 0.0335 *
## arg1 -71.93 857.51 -0.084 0.9332
## op_count:arg0 -62.89 44.65 -1.408 0.1590
## op_count:arg1 -15.81 44.28 -0.357 0.7210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 846900 on 29994 degrees of freedom
## Multiple R-squared: 0.1181, Adjusted R-squared: 0.1179
## F-statistic: 803.1 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP12" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -996408 -581623 -496641 613082 11071100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2387873.35 20420.47 116.935 <0.0000000000000002 ***
## op_count 27257.89 1054.51 25.849 <0.0000000000000002 ***
## arg0 322.22 840.38 0.383 0.701
## arg1 773.92 859.14 0.901 0.368
## op_count:arg0 -6.22 43.40 -0.143 0.886
## op_count:arg1 -24.31 44.37 -0.548 0.584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 871800 on 29994 degrees of freedom
## Multiple R-squared: 0.1238, Adjusted R-squared: 0.1237
## F-statistic: 847.8 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP13" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -991913 -576805 -491979 614450 5057885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2396110.84 20351.89 117.734 <0.0000000000000002 ***
## op_count 26260.45 1050.97 24.987 <0.0000000000000002 ***
## arg0 -632.08 854.45 -0.740 0.459
## arg1 999.80 842.78 1.186 0.236
## op_count:arg0 45.55 44.12 1.032 0.302
## op_count:arg1 -21.65 43.52 -0.497 0.619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 860600 on 29994 degrees of freedom
## Multiple R-squared: 0.1262, Adjusted R-squared: 0.126
## F-statistic: 866 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP14" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -993584 -582643 -481014 596447 5452934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2388902.362 20951.690 114.020 <0.0000000000000002 ***
## op_count 25239.605 1081.941 23.328 <0.0000000000000002 ***
## arg0 938.878 901.890 1.041 0.298
## arg1 259.662 858.265 0.303 0.762
## op_count:arg0 6.658 46.573 0.143 0.886
## op_count:arg1 13.475 44.321 0.304 0.761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 864400 on 29994 degrees of freedom
## Multiple R-squared: 0.1161, Adjusted R-squared: 0.116
## F-statistic: 788 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP15" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1047306 -601637 -499835 662841 7386117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2560150.305 23055.330 111.044 <0.0000000000000002 ***
## op_count 25974.087 1190.572 21.816 <0.0000000000000002 ***
## arg0 412.282 889.838 0.463 0.6431
## arg1 -2192.402 889.683 -2.464 0.0137 *
## op_count:arg0 4.627 45.951 0.101 0.9198
## op_count:arg1 59.508 45.943 1.295 0.1952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 896000 on 29994 degrees of freedom
## Multiple R-squared: 0.1207, Adjusted R-squared: 0.1206
## F-statistic: 823.7 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP16" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1040086 -607321 -499185 640451 12333241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2506500.41 22803.87 109.916 <0.0000000000000002 ***
## op_count 25780.37 1177.59 21.893 <0.0000000000000002 ***
## arg0 1057.30 865.91 1.221 0.222
## arg1 -500.21 934.05 -0.536 0.592
## op_count:arg0 -67.39 44.72 -1.507 0.132
## op_count:arg1 20.55 48.23 0.426 0.670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 908500 on 29994 degrees of freedom
## Multiple R-squared: 0.1024, Adjusted R-squared: 0.1022
## F-statistic: 684 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5216916 -956083 -752761 517683 19398124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4352518.5 76969.9 56.548 < 0.0000000000000002 ***
## op_count 135025.4 3974.7 33.971 < 0.0000000000000002 ***
## arg0 -434.6 2306.2 -0.188 0.851
## arg1 -863.2 2402.7 -0.359 0.719
## arg2 -1269.2 2535.6 -0.501 0.617
## op_count:arg0 3301.9 119.1 27.726 < 0.0000000000000002 ***
## op_count:arg1 3145.3 124.1 25.351 < 0.0000000000000002 ***
## op_count:arg2 -563.9 130.9 -4.307 0.0000166 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2417000 on 29992 degrees of freedom
## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6138
## F-statistic: 6813 on 7 and 29992 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5781141 -936334 -660709 462728 22991750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4406796.3 71368.7 61.747 < 0.0000000000000002 ***
## op_count 85015.9 3685.5 23.068 < 0.0000000000000002 ***
## arg0 -2641.3 2344.2 -1.127 0.260
## arg1 -2797.8 2426.9 -1.153 0.249
## arg2 -1596.6 2393.8 -0.667 0.505
## op_count:arg0 5862.7 121.1 48.431 < 0.0000000000000002 ***
## op_count:arg1 5127.6 125.3 40.914 < 0.0000000000000002 ***
## op_count:arg2 910.8 123.6 7.368 0.000000000000177 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2475000 on 29992 degrees of freedom
## Multiple R-squared: 0.689, Adjusted R-squared: 0.6889
## F-statistic: 9492 on 7 and 29992 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATACOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15107114 -1934970 -1442277 -660844 49861206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4974254.8889 158220.6228 31.439 < 0.0000000000000002 ***
## op_count -5591.1860 8170.4778 -0.684 0.49378
## arg0 -0.3802 11.0433 -0.034 0.97254
## arg1 -0.2241 11.4327 -0.020 0.98436
## arg2 29.5696 10.4767 2.822 0.00477 **
## op_count:arg0 -0.2283 0.5703 -0.400 0.68887
## op_count:arg1 0.4976 0.5904 0.843 0.39930
## op_count:arg2 138.1495 0.5410 255.353 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5647000 on 29992 degrees of freedom
## Multiple R-squared: 0.9195, Adjusted R-squared: 0.9194
## F-statistic: 4.891e+04 on 7 and 29992 DF, p-value: < 0.00000000000000022
##
## [1] "CODECOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8496477 -2074176 -1466956 -732798 58298568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4984579.2788 176653.4442 28.217 <0.0000000000000002 ***
## op_count -16522.0586 9122.3446 -1.811 0.0701 .
## arg0 -6.1938 11.4130 -0.543 0.5873
## arg1 -7.4745 11.2843 -0.662 0.5077
## arg2 32.6095 11.5618 2.820 0.0048 **
## op_count:arg0 0.4906 0.5894 0.832 0.4052
## op_count:arg1 1.2453 0.5827 2.137 0.0326 *
## op_count:arg2 138.1938 0.5970 231.462 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5818000 on 29992 degrees of freedom
## Multiple R-squared: 0.9105, Adjusted R-squared: 0.9105
## F-statistic: 4.359e+04 on 7 and 29992 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATACOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5050481 -2247727 -1799763 -773493 55662894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10563669.5102 167049.2299 63.237 <0.0000000000000002 ***
## op_count 8698.3266 8626.3851 1.008 0.313
## arg0 -2.3435 13.1361 -0.178 0.858
## arg1 5.0963 12.0631 0.422 0.673
## arg2 16.8539 11.8162 1.426 0.154
## op_count:arg0 0.6102 0.6783 0.900 0.368
## op_count:arg1 -0.2363 0.6229 -0.379 0.704
## op_count:arg2 137.0547 0.6102 224.612 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6253000 on 29992 degrees of freedom
## Multiple R-squared: 0.8997, Adjusted R-squared: 0.8997
## F-statistic: 3.844e+04 on 7 and 29992 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2667988 -1028701 -731367 899824 15547075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3604593 28511 126.430 < 0.0000000000000002 ***
## op_count 77385 1004 77.061 < 0.0000000000000002 ***
## arg0 24134 1208 19.981 < 0.0000000000000002 ***
## arg1 9438 1166 8.095 0.000000000000000595 ***
## op_count:expensiveTRUE 53490 1277 41.896 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1655000 on 29995 degrees of freedom
## Multiple R-squared: 0.423, Adjusted R-squared: 0.4229
## F-statistic: 5497 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3495886 -1267206 -736693 349221 22298811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3408927 38376 88.83 <0.0000000000000002 ***
## op_count 130951 1385 94.53 <0.0000000000000002 ***
## arg0 29930 1598 18.73 <0.0000000000000002 ***
## arg1 21192 1601 13.24 <0.0000000000000002 ***
## op_count:expensiveTRUE 68191 1714 39.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2203000 on 29995 degrees of freedom
## Multiple R-squared: 0.4977, Adjusted R-squared: 0.4976
## F-statistic: 7430 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3226217 -1252731 -771650 364716 19174704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3583515 37402 95.811 <0.0000000000000002 ***
## op_count 133663 1361 98.188 <0.0000000000000002 ***
## arg0 26566 1634 16.255 <0.0000000000000002 ***
## arg1 13995 1604 8.726 <0.0000000000000002 ***
## op_count:expensiveTRUE 61719 1716 35.961 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2212000 on 29995 degrees of freedom
## Multiple R-squared: 0.4847, Adjusted R-squared: 0.4846
## F-statistic: 7053 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3445493 -1272284 -759793 402957 21640365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3470760 38159 90.95 <0.0000000000000002 ***
## op_count 132827 1296 102.46 <0.0000000000000002 ***
## arg0 30356 1570 19.34 <0.0000000000000002 ***
## arg1 19181 1640 11.70 <0.0000000000000002 ***
## op_count:expensiveTRUE 66527 1696 39.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2196000 on 29995 degrees of freedom
## Multiple R-squared: 0.4891, Adjusted R-squared: 0.489
## F-statistic: 7179 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4795315 -1257097 -689450 158932 20519733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3047744 50669 60.15 <0.0000000000000002 ***
## op_count 160454 1827 87.84 <0.0000000000000002 ***
## arg0 24500 1497 16.37 <0.0000000000000002 ***
## arg1 20558 1560 13.18 <0.0000000000000002 ***
## arg2 28360 1718 16.50 <0.0000000000000002 ***
## op_count:expensiveTRUE 105343 1943 54.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2364000 on 29994 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6307
## F-statistic: 1.025e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4563522 -1345778 -580327 406749 24792300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1869277 49527 37.74 <0.0000000000000002 ***
## op_count 174246 2232 78.07 <0.0000000000000002 ***
## arg0 54933 1601 34.30 <0.0000000000000002 ***
## arg1 47919 1628 29.44 <0.0000000000000002 ***
## arg2 47425 1664 28.50 <0.0000000000000002 ***
## op_count:expensiveTRUE 127019 2308 55.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2509000 on 29994 degrees of freedom
## Multiple R-squared: 0.6802, Adjusted R-squared: 0.6802
## F-statistic: 1.276e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022proceed_with_opcodes = unique(first_pass[which(first_pass$has_impacting == 'TRUE'), 'opcode'])
models_with_args_automatic = first_pass[which(first_pass$has_impacting == 'TRUE'), c('opcode', 'env')]
models_with_expensive_automatic = first_pass[which(!is.na(first_pass$expensive_ns)), c('opcode', 'env')]
first_pass[which(first_pass$has_impacting == 'TRUE'), ]## opcode env has_significant has_impacting estimate_marginal_ns
## 23 ADD besu TRUE TRUE 59139.7381507484
## 26 DIV besu TRUE TRUE 64440.8136568123
## 27 SDIV besu TRUE TRUE 117636.561326761
## 28 MOD besu TRUE TRUE 106716.358973534
## 29 SMOD besu TRUE TRUE 107143.643084154
## 30 EXP besu TRUE TRUE -65006.0134807013
## 62 ADDMOD besu TRUE TRUE 135025.401335389
## 63 MULMOD besu TRUE TRUE 85015.9150731655
## 64 CALLDATACOPY besu TRUE TRUE -5591.18599445232
## 65 CODECOPY besu TRUE TRUE -16522.0586345979
## 66 RETURNDATACOPY besu TRUE TRUE 8698.3265504224
## arg0_ns arg1_ns arg2_ns expensive_ns
## 23 995.346364457664 1029.11794431736 <NA> <NA>
## 26 3280.95484173404 <NA> <NA> 53490.4167445755
## 27 3813.4619404136 <NA> <NA> 61718.8876260387
## 28 4043.64698117967 <NA> <NA> 68190.6096058811
## 29 4144.17600834556 <NA> <NA> 66527.0478967747
## 30 <NA> 161222.132965171 <NA> <NA>
## 62 3301.93326258818 3145.32858503558 <NA> 105343.306489307
## 63 5862.73515573292 5127.56253678244 <NA> 127018.823017713
## 64 <NA> <NA> 138.149495987096 <NA>
## 65 <NA> <NA> 138.193841682033 <NA>
## 66 <NA> <NA> 137.054701218205 <NA>
## arg0_ns_raw arg1_ns_raw arg2_ns_raw expensive_ns_raw
## 23 995.346364457664 1029.11794431736 <NA> <NA>
## 26 3280.95484173404 -886.715655686377 <NA> 53490.4167445755
## 27 3813.4619404136 -1002.85974373072 <NA> 61718.8876260387
## 28 4043.64698117967 -612.852985723579 <NA> 68190.6096058811
## 29 4144.17600834556 -622.302702197993 <NA> 66527.0478967747
## 30 27401.8013551266 161222.132965171 <NA> <NA>
## 62 3301.93326258818 3145.32858503558 -563.939160832652 105343.306489307
## 63 5862.73515573292 5127.56253678244 910.816302457666 127018.823017713
## 64 -0.228337140849684 0.497624449467041 138.149495987096 <NA>
## 65 0.49056884283877 1.24526323664146 138.193841682033 <NA>
## 66 0.610182649790944 -0.236270137314065 137.054701218205 <NA>
## arg0_ns_p
## 23 0.000000000000000000000000000000000917354071418478
## 26 3.28435473981944e-317
## 27 2.06403979558713e-245
## 28 1.02320297825666e-276
## 29 1.01085126358467e-306
## 30 0.000000000000000000415945203533126
## 62 4.39209729822195e-167
## 63 0
## 64 0.688865395380998
## 65 0.405207676519483
## 66 0.368385662701539
## arg1_ns_p
## 23 0.000000000000000000000000000000000000144651867702733
## 26 0.0000000000000000000000000180727443383065
## 27 0.00000000000000000104671059335752
## 28 0.0000000682395797100088
## 29 0.000000182090452412657
## 30 0
## 62 2.65377869638366e-140
## 63 0
## 64 0.399299123027796
## 65 0.0326064384982231
## 66 0.704479981635845
## arg2_ns_p expensive_ns_p
## 23 <NA> <NA>
## 26 <NA> 0
## 27 <NA> 2.64958013758168e-277
## 28 <NA> 0
## 29 <NA> 0
## 30 <NA> <NA>
## 62 0.0000166066591397799 0
## 63 0.00000000000017740396346513 0
## 64 0 <NA>
## 65 0 <NA>
## 66 0 <NA>We inspect the automatic choice of models, but then coerce the choice
to a fixed list. We drop the division OPCODEs (DIV etc.),
because their arguments only seem to have an indirect impact via the
fact that x / y is trivial if x < y. This makes the
DIV(x, y) appear costlier for large x and cheaper for large
y.
models_with_args = data.frame(opcode="EXP", env=env, arg=1)
first_pass$arg1_ns[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env] <- first_pass$arg1_ns_raw[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CALLDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CODECOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="RETURNDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env]
models_with_expensive = data.frame(opcode="DIV", env=env)
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SDIV", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="ADDMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MULMOD", env=env))Detailed analysis for selected OPCODEs
We go through all the OPCODEs which turned out to have impacting arguments in the automatic discrimination procedure, and we plot some validation plots to inspect these relationships.
# Takes the results data frame and checks which argument indices (0, 1, etc.)
# turned out to be impacting
get_impact_args_for <- function(df, opcode, env) {
if (opcode %in% nullary_opcodes) {
return(c())
}
args = c()
for (n in 0:2) {
argname = paste0('arg', n, '_ns')
if (!is.na(df[which(df$opcode==opcode & df$env==env), argname])) {
args = c(n, args)
}
}
return(rev(args))
}
# same as `get_impact_args_for` but gets all the argument indices
get_args_for <- function(df, opcode, env) {
if (opcode %in% unary_opcodes) {
c(0)
} else if (opcode %in% binary_opcodes) {
c(0, 1)
} else if (opcode %in% ternary_opcodes) {
c(0, 1, 2)
}
}
# Builds a final model formula to estimate, based on whether the arguments
# came out impactful from the automatic discrimination process.
get_model_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' + ')
impact_args = get_impact_args_for(df, opcode, env)
if (opcode %in% nullary_opcodes) {
as.formula('measure_total_time_ns ~ op_count')
} else if (is.null(impact_args)) {
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula))
} else {
arg_op_count_names = paste0('arg', impact_args, ':op_count')
arg_op_counts_formula = paste0(arg_op_count_names, collapse=' + ')
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula, ' + ', arg_op_counts_formula))
}
}
# Same as `get_model_formula_for` but gauged towards the division OPCODEs specifically.
get_expensive_model_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' + ')
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula, ' + expensive:op_count'))
}
# Same as `get_model_formula_for` but returns the formula to provide the `aggregate` function with.
get_aggregate_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' * ')
as.formula(paste0('measure_total_time_ns ~ op_count * env * opcode * ', args_formula))
}
# Presents the diagnostic plots for a given slice of the data
plot_model <- function(df, opcode, env, use_mean) {
if (missing(use_mean)) {
use_mean = FALSE
}
if (use_mean) {
df = aggregate(get_aggregate_formula_for(df, opcode, env), measurements[which(df$opcode==opcode & df$env==env), ], mean, na.action=na.pass)
}
model = arg_lm(df, opcode, env, get_model_formula_for(first_pass, opcode, env))
print(c(opcode, env))
print(summary(model))
par(mfrow=c(2,2))
plot(model)
plot_data = df[which(df$env == env & df$opcode == opcode & df$op_count == max(df$op_count)), ]
if (opcode %in% binary_opcodes) {
par(mfrow=c(1,1))
decreasing_colors = heat.colors(nrow(plot_data))
plot_data=plot_data[order(plot_data$measure_total_time_ns, decreasing=TRUE), ]
with(plot_data, plot(arg0, arg1, col=decreasing_colors, pch=19))
}
title(main=paste(opcode, env))
}Using the functions defined above, we proceed to plot the diagnostic plots of the arguments models.
for (env in all_envs) {
for (opcode in proceed_with_opcodes) {
plot_model(measurements, opcode, env, use_mean=TRUE)
}
}## [1] "ADD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -311229 -97213 -13692 82015 632443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4143543.87 25228.48 164.241 <0.0000000000000002 ***
## op_count 58952.29 1302.79 45.251 <0.0000000000000002 ***
## arg0 1239.62 996.15 1.244 0.214
## arg1 -293.04 980.70 -0.299 0.765
## op_count:arg0 1004.33 51.44 19.524 <0.0000000000000002 ***
## op_count:arg1 1042.80 50.64 20.591 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 133800 on 528 degrees of freedom
## Multiple R-squared: 0.9873, Adjusted R-squared: 0.9872
## F-statistic: 8217 on 5 and 528 DF, p-value: < 0.00000000000000022
## [1] "DIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1405120 -214343 -3010 248981 1157267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4408356.7 61906.5 71.210 <0.0000000000000002 ***
## op_count 50088.0 2798.7 17.897 <0.0000000000000002 ***
## arg0 647.9 2895.3 0.224 0.823
## arg1 -15554.4 1773.6 -8.770 <0.0000000000000002 ***
## op_count:arg0 3250.4 149.5 21.740 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 390600 on 547 degrees of freedom
## Multiple R-squared: 0.9282, Adjusted R-squared: 0.9276
## F-statistic: 1767 on 4 and 547 DF, p-value: < 0.00000000000000022
## [1] "SDIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1957906 -282707 -41229 338623 2069148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4480895 84495 53.031 < 0.0000000000000002 ***
## op_count 102730 3843 26.729 < 0.0000000000000002 ***
## arg0 1457 3952 0.369 0.713
## arg1 -14986 2538 -5.905 0.00000000632 ***
## op_count:arg0 3717 204 18.220 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 537200 on 529 degrees of freedom
## Multiple R-squared: 0.9401, Adjusted R-squared: 0.9397
## F-statistic: 2077 on 4 and 529 DF, p-value: < 0.00000000000000022
## [1] "MOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2103617 -261751 -77173 432410 1353184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4385561.3 95693.3 45.829 < 0.0000000000000002 ***
## op_count 96205.3 4380.2 21.963 < 0.0000000000000002 ***
## arg0 1595.5 4346.3 0.367 0.713698
## arg1 -9314.5 2774.9 -3.357 0.000846 ***
## op_count:arg0 4153.6 224.4 18.508 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 588100 on 523 degrees of freedom
## Multiple R-squared: 0.9333, Adjusted R-squared: 0.9328
## F-statistic: 1830 on 4 and 523 DF, p-value: < 0.00000000000000022
## [1] "SMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2064263 -229388 -56209 374149 1410952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4398303.2 87892.7 50.042 < 0.0000000000000002 ***
## op_count 96889.7 3841.3 25.223 < 0.0000000000000002 ***
## arg0 -211.8 4005.0 -0.053 0.95785
## arg1 -8070.1 2745.6 -2.939 0.00343 **
## op_count:arg0 4192.3 206.6 20.293 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 561300 on 550 degrees of freedom
## Multiple R-squared: 0.9369, Adjusted R-squared: 0.9364
## F-statistic: 2040 on 4 and 550 DF, p-value: < 0.00000000000000022
## [1] "EXP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -141749734 -26729920 -821390 8086960 202423443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1092082 8966723 0.122 0.903
## op_count 373932 410840 0.910 0.363
## arg0 213388 278090 0.767 0.443
## arg1 -21607 422329 -0.051 0.959
## op_count:arg1 164786 21768 7.570 0.000000000000168 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 59920000 on 526 degrees of freedom
## Multiple R-squared: 0.3984, Adjusted R-squared: 0.3938
## F-statistic: 87.07 on 4 and 526 DF, p-value: < 0.00000000000000022
## [1] "ADDMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3715155 -316482 -73133 586645 1760055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4506828.0 169745.7 26.550 <0.0000000000000002 ***
## op_count 124738.1 7916.3 15.757 <0.0000000000000002 ***
## arg0 -596.9 5745.8 -0.104 0.9173
## arg1 -1550.5 5973.6 -0.260 0.7953
## arg2 -9728.3 3995.9 -2.435 0.0152 *
## op_count:arg0 3312.8 296.7 11.166 <0.0000000000000002 ***
## op_count:arg1 3191.1 308.0 10.360 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 851700 on 593 degrees of freedom
## Multiple R-squared: 0.9283, Adjusted R-squared: 0.9276
## F-statistic: 1280 on 6 and 593 DF, p-value: < 0.00000000000000022
## [1] "MULMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4525175 -286269 -25308 685056 1613798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4185313.5 171333.9 24.428 < 0.0000000000000002 ***
## op_count 100166.0 8223.1 12.181 < 0.0000000000000002 ***
## arg0 -2151.2 6203.0 -0.347 0.72887
## arg1 -3191.0 6386.2 -0.500 0.61749
## arg2 12396.8 3997.0 3.102 0.00202 **
## op_count:arg0 5800.9 320.2 18.114 < 0.0000000000000002 ***
## op_count:arg1 5149.1 329.7 15.616 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 921100 on 590 degrees of freedom
## Multiple R-squared: 0.9419, Adjusted R-squared: 0.9413
## F-statistic: 1594 on 6 and 590 DF, p-value: < 0.00000000000000022
## [1] "CALLDATACOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2504934 -342990 -7740 383485 2042814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4935826.9573 104877.8221 47.063 < 0.0000000000000002 ***
## op_count -3029.3239 4250.2438 -0.713 0.476286
## arg0 -3.8052 5.8124 -0.655 0.512928
## arg1 7.2403 6.0173 1.203 0.229362
## arg2 30.2297 8.6966 3.476 0.000546 ***
## op_count:arg2 138.1055 0.4483 308.042 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 664600 on 594 degrees of freedom
## Multiple R-squared: 0.9988, Adjusted R-squared: 0.9988
## F-statistic: 9.887e+04 on 5 and 594 DF, p-value: < 0.00000000000000022
## [1] "CODECOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2491115 -335076 9448 388919 1973635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4762131.797 110749.848 42.999 < 0.0000000000000002 ***
## op_count -1692.226 4403.488 -0.384 0.700899
## arg0 1.165 5.743 0.203 0.839358
## arg1 11.204 5.678 1.973 0.048945 *
## arg2 35.165 9.152 3.842 0.000135 ***
## op_count:arg2 138.024 0.471 293.038 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 654600 on 594 degrees of freedom
## Multiple R-squared: 0.9988, Adjusted R-squared: 0.9988
## F-statistic: 9.639e+04 on 5 and 594 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATACOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2754809 -400024 -3629 492052 1931178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10530020.3647 110994.8958 94.869 <0.0000000000000002 ***
## op_count 10941.6029 4652.3297 2.352 0.0190 *
## arg0 6.8092 6.7873 1.003 0.3162
## arg1 1.5522 6.2329 0.249 0.8034
## arg2 16.4646 9.6428 1.707 0.0883 .
## op_count:arg2 137.0807 0.4976 275.492 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 722400 on 594 degrees of freedom
## Multiple R-squared: 0.9985, Adjusted R-squared: 0.9985
## F-statistic: 8.063e+04 on 5 and 594 DF, p-value: < 0.00000000000000022
Producing the final estimates
We’d like to only estimate using the arg-variables in models, where this actually matters to avoid spurious impact of insignificant variables.
We’ll estimate a model with only those argument variables, where they turned out impacting. For those where no argument variable was impacting, we’ll only estimate the marginal increase (corresponding to the constant cost of an OPCODE).
# `results_df` is assumed to have the columns as the `estimates` data frame has (see below)
add_non_arg_model_estimates <- function(model, results_df, env, opcode) {
pure_op_count_coeff = summary(model)$coefficients["op_count", 1]
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
results_df[nrow(results_df) + 1, ] = c(opcode, env, FALSE, FALSE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
return(results_df)
}
add_arg_model_estimates <- function(model, opcode, env, results_df, df) {
all_coefficients = summary(model)$coefficients
arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
pure_op_count_coeff = all_coefficients["op_count", 1]
# will be filled if any is impacting
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
impact_args = get_impact_args_for(df, opcode, env)
arg_op_count_names = paste0('op_count:arg', impact_args)
args_ns[impact_args + 1] = all_coefficients[arg_op_count_names, 'Estimate']
args_ns_stderr[impact_args + 1] = all_coefficients[arg_op_count_names, 'Std. Error']
results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
return(results_df)
}
add_expensive_model_estimates <- function(model, opcode, env, results_df, df) {
all_coefficients = summary(model)$coefficients
pure_op_count_coeff = all_coefficients["op_count", 1]
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
expensive = all_coefficients['op_count:expensiveTRUE', 'Estimate']
expensive_stderr = all_coefficients['op_count:expensiveTRUE', 'Std. Error']
results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, expensive, args_ns_stderr, expensive_stderr)
return(results_df)
}estimates = data.frame(matrix(ncol = 13, nrow = 0))
colnames(estimates) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns', 'arg0_ns_stderr', 'arg1_ns_stderr', 'arg2_ns_stderr', 'expensive_ns_stderr')
for (env in all_envs) {
for (opcode in all_opcodes) {
is_modeled_with_args = nrow(merge(data.frame(opcode=opcode, env=env), models_with_args)) > 0
is_modeled_with_expensive = nrow(merge(data.frame(opcode=opcode, env=env), models_with_expensive)) > 0
if (is_modeled_with_expensive) {
model = arg_lm(measurements, opcode, env, get_expensive_model_formula_for(first_pass, opcode, env))
estimates = add_expensive_model_estimates(model, opcode, env, estimates, first_pass)
} else if (is_modeled_with_args) {
model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
estimates = add_arg_model_estimates(model, opcode, env, estimates, first_pass)
} else {
model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
estimates = add_non_arg_model_estimates(model, estimates, env, opcode)
}
print(c(opcode, env))
print(summary(model))
}
}## [1] "ADD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1452551 -971335 -772328 861188 17081974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4141012.16 40336.52 102.662 <0.0000000000000002 ***
## op_count 59139.74 2082.97 28.392 <0.0000000000000002 ***
## arg0 1286.96 1589.48 0.810 0.418
## arg1 -115.45 1553.61 -0.074 0.941
## op_count:arg0 995.35 82.08 12.126 <0.0000000000000002 ***
## op_count:arg1 1029.12 80.23 12.827 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1569000 on 29994 degrees of freedom
## Multiple R-squared: 0.3587, Adjusted R-squared: 0.3586
## F-statistic: 3356 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MUL" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1333434 -968248 -748914 822791 18343718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4144193.8 28097.4 147.494 <0.0000000000000002 ***
## op_count 90509.6 745.4 121.425 <0.0000000000000002 ***
## arg0 -307.5 1019.1 -0.302 0.763
## arg1 -142.8 1021.3 -0.140 0.889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1581000 on 29996 degrees of freedom
## Multiple R-squared: 0.3296, Adjusted R-squared: 0.3295
## F-statistic: 4915 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SUB" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1233256 -941461 -761567 882688 9947946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4154179.0 26642.2 155.925 <0.0000000000000002 ***
## op_count 57096.7 703.0 81.214 <0.0000000000000002 ***
## arg0 -329.9 965.1 -0.342 0.732
## arg1 311.7 960.3 0.325 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1491000 on 29996 degrees of freedom
## Multiple R-squared: 0.1803, Adjusted R-squared: 0.1802
## F-statistic: 2199 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2667988 -1028701 -731367 899824 15547075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3604593 28511 126.430 < 0.0000000000000002 ***
## op_count 77385 1004 77.061 < 0.0000000000000002 ***
## arg0 24134 1208 19.981 < 0.0000000000000002 ***
## arg1 9438 1166 8.095 0.000000000000000595 ***
## op_count:expensiveTRUE 53490 1277 41.896 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1655000 on 29995 degrees of freedom
## Multiple R-squared: 0.423, Adjusted R-squared: 0.4229
## F-statistic: 5497 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3226217 -1252731 -771650 364716 19174704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3583515 37402 95.811 <0.0000000000000002 ***
## op_count 133663 1361 98.188 <0.0000000000000002 ***
## arg0 26566 1634 16.255 <0.0000000000000002 ***
## arg1 13995 1604 8.726 <0.0000000000000002 ***
## op_count:expensiveTRUE 61719 1716 35.961 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2212000 on 29995 degrees of freedom
## Multiple R-squared: 0.4847, Adjusted R-squared: 0.4846
## F-statistic: 7053 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3495886 -1267206 -736693 349221 22298811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3408927 38376 88.83 <0.0000000000000002 ***
## op_count 130951 1385 94.53 <0.0000000000000002 ***
## arg0 29930 1598 18.73 <0.0000000000000002 ***
## arg1 21192 1601 13.24 <0.0000000000000002 ***
## op_count:expensiveTRUE 68191 1714 39.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2203000 on 29995 degrees of freedom
## Multiple R-squared: 0.4977, Adjusted R-squared: 0.4976
## F-statistic: 7430 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3445493 -1272284 -759793 402957 21640365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3470760 38159 90.95 <0.0000000000000002 ***
## op_count 132827 1296 102.46 <0.0000000000000002 ***
## arg0 30356 1570 19.34 <0.0000000000000002 ***
## arg1 19181 1640 11.70 <0.0000000000000002 ***
## op_count:expensiveTRUE 66527 1696 39.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2196000 on 29995 degrees of freedom
## Multiple R-squared: 0.4891, Adjusted R-squared: 0.489
## F-statistic: 7179 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4795315 -1257097 -689450 158932 20519733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3047744 50669 60.15 <0.0000000000000002 ***
## op_count 160454 1827 87.84 <0.0000000000000002 ***
## arg0 24500 1497 16.37 <0.0000000000000002 ***
## arg1 20558 1560 13.18 <0.0000000000000002 ***
## arg2 28360 1718 16.50 <0.0000000000000002 ***
## op_count:expensiveTRUE 105343 1943 54.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2364000 on 29994 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6307
## F-statistic: 1.025e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4563522 -1345778 -580327 406749 24792300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1869277 49527 37.74 <0.0000000000000002 ***
## op_count 174246 2232 78.07 <0.0000000000000002 ***
## arg0 54933 1601 34.30 <0.0000000000000002 ***
## arg1 47919 1628 29.44 <0.0000000000000002 ***
## arg2 47425 1664 28.50 <0.0000000000000002 ***
## op_count:expensiveTRUE 127019 2308 55.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2509000 on 29994 degrees of freedom
## Multiple R-squared: 0.6802, Adjusted R-squared: 0.6802
## F-statistic: 1.276e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "EXP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -142766432 -29784969 -1694531 7186457 238703191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1845061 1216356 -1.517 0.129
## op_count 343496 55747 6.162 0.000000000729 ***
## arg0 409575 37594 10.895 < 0.0000000000000002 ***
## arg1 -36807 57241 -0.643 0.520
## op_count:arg1 163575 2951 55.427 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 61290000 on 29995 degrees of freedom
## Multiple R-squared: 0.3812, Adjusted R-squared: 0.3811
## F-statistic: 4619 on 4 and 29995 DF, p-value: < 0.00000000000000022
##
## [1] "SIGNEXTEND" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1313668 -970724 -760188 822479 16467828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4158888.1 26394.2 157.568 <0.0000000000000002 ***
## op_count 145660.9 737.1 197.609 <0.0000000000000002 ***
## arg0 -333.8 963.9 -0.346 0.729
## arg1 -441.8 994.2 -0.444 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1564000 on 29996 degrees of freedom
## Multiple R-squared: 0.5656, Adjusted R-squared: 0.5655
## F-statistic: 1.302e+04 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "LT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1176309 -906886 -749931 1016181 11119971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4164563.9 24771.2 168.121 <0.0000000000000002 ***
## op_count 52897.5 659.3 80.236 <0.0000000000000002 ***
## arg0 -462.8 896.6 -0.516 0.606
## arg1 -701.6 922.3 -0.761 0.447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1399000 on 29996 degrees of freedom
## Multiple R-squared: 0.1767, Adjusted R-squared: 0.1766
## F-statistic: 2146 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "GT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1190176 -916936 -756815 1019627 8717342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4163368.8 24661.0 168.824 <0.0000000000000002 ***
## op_count 53247.0 672.6 79.163 <0.0000000000000002 ***
## arg0 -371.0 945.2 -0.392 0.695
## arg1 -136.9 914.9 -0.150 0.881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1427000 on 29996 degrees of freedom
## Multiple R-squared: 0.1728, Adjusted R-squared: 0.1727
## F-statistic: 2089 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SLT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1341122 -869595 -746707 1091397 11142482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3770539.7 24625.2 153.12 <0.0000000000000002 ***
## op_count 67747.9 630.6 107.44 <0.0000000000000002 ***
## arg0 10452.8 852.1 12.27 <0.0000000000000002 ***
## arg1 11181.1 820.8 13.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1338000 on 29996 degrees of freedom
## Multiple R-squared: 0.2831, Adjusted R-squared: 0.283
## F-statistic: 3948 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SGT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1324921 -873182 -754144 1102368 15804534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3773734.8 23471.4 160.78 <0.0000000000000002 ***
## op_count 66898.9 632.5 105.77 <0.0000000000000002 ***
## arg0 10732.1 915.1 11.73 <0.0000000000000002 ***
## arg1 11409.9 827.9 13.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1342000 on 29996 degrees of freedom
## Multiple R-squared: 0.2774, Adjusted R-squared: 0.2773
## F-statistic: 3838 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "EQ" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1220540 -909152 -753383 1010982 18396553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4165610.2 24215.2 172.025 <0.0000000000000002 ***
## op_count 51899.3 668.4 77.646 <0.0000000000000002 ***
## arg0 -270.4 878.8 -0.308 0.758
## arg1 -603.6 922.8 -0.654 0.513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1418000 on 29996 degrees of freedom
## Multiple R-squared: 0.1674, Adjusted R-squared: 0.1673
## F-statistic: 2010 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "ISZERO" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1059257 -847248 -728291 995444 14083973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4142100.02 16809.82 246.410 <0.0000000000000002 ***
## op_count 31369.04 588.40 53.313 <0.0000000000000002 ***
## arg0 70.07 800.18 0.088 0.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1248000 on 29997 degrees of freedom
## Multiple R-squared: 0.08655, Adjusted R-squared: 0.08649
## F-statistic: 1421 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "AND" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1229432 -937149 -751462 890017 12365489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4143966.96 25100.98 165.092 <0.0000000000000002 ***
## op_count 55993.43 702.09 79.752 <0.0000000000000002 ***
## arg0 -81.38 902.59 -0.090 0.928
## arg1 85.18 981.30 0.087 0.931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1489000 on 29996 degrees of freedom
## Multiple R-squared: 0.1749, Adjusted R-squared: 0.1749
## F-statistic: 2120 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "OR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1228345 -943982 -768150 896989 18286477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4173429.1 25835.8 161.537 <0.0000000000000002 ***
## op_count 55139.0 703.1 78.419 <0.0000000000000002 ***
## arg0 -124.6 950.9 -0.131 0.896
## arg1 -410.1 949.3 -0.432 0.666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1492000 on 29996 degrees of freedom
## Multiple R-squared: 0.1701, Adjusted R-squared: 0.1701
## F-statistic: 2050 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "XOR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1234752 -939170 -755715 878284 9924996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4160688.1 25182.9 165.219 <0.0000000000000002 ***
## op_count 55741.3 703.3 79.262 <0.0000000000000002 ***
## arg0 -286.3 907.8 -0.315 0.752
## arg1 -366.1 917.4 -0.399 0.690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1492000 on 29996 degrees of freedom
## Multiple R-squared: 0.1732, Adjusted R-squared: 0.1731
## F-statistic: 2094 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "NOT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1102030 -863900 -736708 1125299 12082562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4147412.8 19068.8 217.498 <0.0000000000000002 ***
## op_count 34886.7 632.9 55.120 <0.0000000000000002 ***
## arg0 -446.4 851.8 -0.524 0.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1343000 on 29997 degrees of freedom
## Multiple R-squared: 0.09198, Adjusted R-squared: 0.09192
## F-statistic: 1519 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "BYTE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1208132 -917399 -750738 1088620 10019143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4153457.3 25384.7 163.621 <0.0000000000000002 ***
## op_count 52101.6 667.5 78.054 <0.0000000000000002 ***
## arg0 -1634.0 924.3 -1.768 0.0771 .
## arg1 1428.9 907.3 1.575 0.1153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1416000 on 29996 degrees of freedom
## Multiple R-squared: 0.169, Adjusted R-squared: 0.1689
## F-statistic: 2033 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SHL" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -962898 -769276 -671789 853774 10908588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4248566.8 19016.2 223.42 <0.0000000000000002 ***
## op_count 22002.2 525.7 41.85 <0.0000000000000002 ***
## arg0 -7457.0 683.0 -10.92 <0.0000000000000002 ***
## arg1 447.3 709.9 0.63 0.529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1115000 on 29996 degrees of freedom
## Multiple R-squared: 0.05872, Adjusted R-squared: 0.05863
## F-statistic: 623.8 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SHR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1004547 -786369 -672884 841053 10242259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4230215.9 18260.6 231.658 <0.0000000000000002 ***
## op_count 23305.9 533.7 43.669 <0.0000000000000002 ***
## arg0 -8424.6 717.5 -11.741 <0.0000000000000002 ***
## arg1 1753.5 705.0 2.487 0.0129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1132000 on 29996 degrees of freedom
## Multiple R-squared: 0.06392, Adjusted R-squared: 0.06382
## F-statistic: 682.7 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SAR" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -940128 -757434 -687537 851209 7422649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4191002.7 20476.1 204.678 < 0.0000000000000002 ***
## op_count 22043.0 522.9 42.156 < 0.0000000000000002 ***
## arg0 -3756.7 722.4 -5.200 0.000000201 ***
## arg1 258.8 713.5 0.363 0.717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1109000 on 29996 degrees of freedom
## Multiple R-squared: 0.05676, Adjusted R-squared: 0.05666
## F-statistic: 601.6 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "ADDRESS" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -404402 -301396 -168329 153195 14981906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1676462.6 4054.4 413.49 <0.0000000000000002 ***
## op_count 9322.9 209.4 44.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 444100 on 29998 degrees of freedom
## Multiple R-squared: 0.062, Adjusted R-squared: 0.06197
## F-statistic: 1983 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "ORIGIN" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -402856 -302527 -159081 151402 5851015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1676466.3 3986.7 420.52 <0.0000000000000002 ***
## op_count 9405.7 205.9 45.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 436700 on 29998 degrees of freedom
## Multiple R-squared: 0.06506, Adjusted R-squared: 0.06502
## F-statistic: 2087 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "CALLER" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -396326 -302028 -175656 153106 3567492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1681337.2 3966.6 423.87 <0.0000000000000002 ***
## op_count 9000.2 204.8 43.94 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 434500 on 29998 degrees of freedom
## Multiple R-squared: 0.06047, Adjusted R-squared: 0.06044
## F-statistic: 1931 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "CALLVALUE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -396220 -297679 -172857 158354 3582012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1669785.0 3884.6 429.85 <0.0000000000000002 ***
## op_count 9593.6 200.6 47.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 425500 on 29998 degrees of freedom
## Multiple R-squared: 0.07084, Adjusted R-squared: 0.07081
## F-statistic: 2287 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATALOAD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1511021 -1274011 -1205277 -1046574 27989872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6431117.989 40088.576 160.423 <0.0000000000000002 ***
## op_count 59845.782 1353.789 44.206 <0.0000000000000002 ***
## arg0 3.785 3.450 1.097 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2872000 on 29997 degrees of freedom
## Multiple R-squared: 0.0612, Adjusted R-squared: 0.06113
## F-statistic: 977.7 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATASIZE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -551166 -381336 -257011 302787 3846508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1675719.0 4792.6 349.65 <0.0000000000000002 ***
## op_count 16991.9 247.5 68.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 525000 on 29998 degrees of freedom
## Multiple R-squared: 0.1358, Adjusted R-squared: 0.1358
## F-statistic: 4714 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATACOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15105760 -1936487 -1439525 -659530 49881678
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4935826.9573 126023.1385 39.166 < 0.0000000000000002 ***
## op_count -3029.3239 5107.1718 -0.593 0.55308
## arg0 -3.8052 6.9843 -0.545 0.58587
## arg1 7.2403 7.2305 1.001 0.31667
## arg2 30.2297 10.4500 2.893 0.00382 **
## op_count:arg2 138.1055 0.5387 256.356 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5647000 on 29994 degrees of freedom
## Multiple R-squared: 0.9194, Adjusted R-squared: 0.9194
## F-statistic: 6.847e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "CODESIZE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -553586 -379600 -252475 297535 6920869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1673002.5 4833.3 346.14 <0.0000000000000002 ***
## op_count 17442.7 249.6 69.89 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 529500 on 29998 degrees of freedom
## Multiple R-squared: 0.14, Adjusted R-squared: 0.14
## F-statistic: 4884 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "CODECOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8496477 -2069935 -1465955 -738496 58298568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4762131.797 139197.282 34.211 < 0.0000000000000002 ***
## op_count -1692.226 5534.577 -0.306 0.75979
## arg0 1.165 7.219 0.161 0.87181
## arg1 11.204 7.137 1.570 0.11645
## arg2 35.165 11.503 3.057 0.00224 **
## op_count:arg2 138.024 0.592 233.150 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5818000 on 29994 degrees of freedom
## Multiple R-squared: 0.9105, Adjusted R-squared: 0.9105
## F-statistic: 6.101e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "GASPRICE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -400427 -296037 -164932 157549 3366029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1673171 3834 436.36 <0.0000000000000002 ***
## op_count 9332 198 47.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 420000 on 29998 degrees of freedom
## Multiple R-squared: 0.06894, Adjusted R-squared: 0.06891
## F-statistic: 2221 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATASIZE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -394089 -298509 -180995 155228 5094927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1675706 3893 430.49 <0.0000000000000002 ***
## op_count 9722 201 48.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 426400 on 29998 degrees of freedom
## Multiple R-squared: 0.07233, Adjusted R-squared: 0.0723
## F-statistic: 2339 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATACOPY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5092469 -2247781 -1798963 -769169 55662894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10530020.3647 135860.6034 77.506 <0.0000000000000002 ***
## op_count 10941.6029 5694.5710 1.921 0.0547 .
## arg0 6.8092 8.3079 0.820 0.4124
## arg1 1.5522 7.6293 0.203 0.8388
## arg2 16.4646 11.8030 1.395 0.1630
## op_count:arg2 137.0807 0.6091 225.070 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6253000 on 29994 degrees of freedom
## Multiple R-squared: 0.8997, Adjusted R-squared: 0.8997
## F-statistic: 5.382e+04 on 5 and 29994 DF, p-value: < 0.00000000000000022
##
## [1] "COINBASE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -400260 -300449 -174975 153341 9524503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1679696.7 3972.9 422.79 <0.0000000000000002 ***
## op_count 9094.4 205.2 44.33 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 435200 on 29998 degrees of freedom
## Multiple R-squared: 0.06148, Adjusted R-squared: 0.06145
## F-statistic: 1965 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "TIMESTAMP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -566886 -381227 -257874 304919 6123200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1680439.0 4796.0 350.38 <0.0000000000000002 ***
## op_count 16775.7 247.7 67.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 525400 on 29998 degrees of freedom
## Multiple R-squared: 0.1327, Adjusted R-squared: 0.1326
## F-statistic: 4588 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "NUMBER" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -542724 -370400 -243923 260081 4163637
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1682044.1 4695.8 358.2 <0.0000000000000002 ***
## op_count 16343.5 242.5 67.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 514400 on 29998 degrees of freedom
## Multiple R-squared: 0.1315, Adjusted R-squared: 0.1315
## F-statistic: 4543 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "DIFFICULTY" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -401937 -298581 -165886 155320 3313286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1675930.4 3887.3 431.13 <0.0000000000000002 ***
## op_count 9616.6 200.7 47.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 425800 on 29998 degrees of freedom
## Multiple R-squared: 0.07107, Adjusted R-squared: 0.07104
## F-statistic: 2295 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "GASLIMIT" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -540265 -369121 -242990 259157 17095537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1676121.3 4726.5 354.62 <0.0000000000000002 ***
## op_count 16541.8 244.1 67.77 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 517800 on 29998 degrees of freedom
## Multiple R-squared: 0.1328, Adjusted R-squared: 0.1328
## F-statistic: 4593 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "CHAINID" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -395257 -297795 -174832 158706 3167024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1676926.9 3849.0 435.68 <0.0000000000000002 ***
## op_count 9332.9 198.8 46.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 421600 on 29998 degrees of freedom
## Multiple R-squared: 0.06847, Adjusted R-squared: 0.06844
## F-statistic: 2205 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "SELFBALANCE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2198770 -1360652 -580952 -139795 22668269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1957245 19700 99.35 <0.0000000000000002 ***
## op_count 438728 1017 431.27 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2158000 on 29998 degrees of freedom
## Multiple R-squared: 0.8611, Adjusted R-squared: 0.8611
## F-statistic: 1.86e+05 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "POP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1003736 -571805 -472635 598033 5490854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2349846.3 11516.9 204.03 < 0.0000000000000002 ***
## op_count 21192.7 394.8 53.68 < 0.0000000000000002 ***
## arg0 2906.6 516.2 5.63 0.0000000181 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 837500 on 29997 degrees of freedom
## Multiple R-squared: 0.08852, Adjusted R-squared: 0.08846
## F-statistic: 1457 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "MLOAD" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1373617 -1186183 -1129881 -949064 22814158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6418062.172 38967.570 164.703 <0.0000000000000002 ***
## op_count 24847.635 1284.636 19.342 <0.0000000000000002 ***
## arg0 -1.716 3.309 -0.518 0.604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2725000 on 29997 degrees of freedom
## Multiple R-squared: 0.01233, Adjusted R-squared: 0.01226
## F-statistic: 187.2 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1623071 -1306100 -1121725 -920022 15268993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5015816.9300 47421.8266 105.770 <0.0000000000000002 ***
## op_count 33765.0044 1292.8230 26.117 <0.0000000000000002 ***
## arg0 3.2140 3.3543 0.958 0.338
## arg1 0.5199 3.2830 0.158 0.874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2742000 on 29996 degrees of freedom
## Multiple R-squared: 0.02226, Adjusted R-squared: 0.02217
## F-statistic: 227.7 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1633584 -1305404 -1219878 -884979 18765166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4968599.3224 44472.7843 111.722 <0.0000000000000002 ***
## op_count 24950.9339 1266.4128 19.702 <0.0000000000000002 ***
## arg0 2.9769 3.3735 0.882 0.378
## arg1 -0.8502 3.3897 -0.251 0.802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2686000 on 29996 degrees of freedom
## Multiple R-squared: 0.0128, Adjusted R-squared: 0.0127
## F-statistic: 129.7 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "JUMP" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -487288 -309194 -204659 197488 9664457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1616946.5 4175.8 387.2 <0.0000000000000002 ***
## op_count 15935.1 215.6 73.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 457400 on 29998 degrees of freedom
## Multiple R-squared: 0.154, Adjusted R-squared: 0.154
## F-statistic: 5461 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPI" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -918714 -754217 -704047 890547 11453028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4076167.1 14942.1 272.798 <0.0000000000000002 ***
## op_count 19058.5 528.9 36.037 <0.0000000000000002 ***
## arg0 415.3 682.6 0.608 0.543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1122000 on 29997 degrees of freedom
## Multiple R-squared: 0.04151, Adjusted R-squared: 0.04144
## F-statistic: 649.5 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "PC" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -555390 -378942 -256075 304330 4244186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1675541.1 4778.9 350.61 <0.0000000000000002 ***
## op_count 16794.3 246.8 68.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 523500 on 29998 degrees of freedom
## Multiple R-squared: 0.1337, Adjusted R-squared: 0.1337
## F-statistic: 4631 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "MSIZE" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -396879 -296879 -167778 157907 3669499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1673195.4 3844.0 435.27 <0.0000000000000002 ***
## op_count 9665.5 198.5 48.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 421100 on 29998 degrees of freedom
## Multiple R-squared: 0.07325, Adjusted R-squared: 0.07322
## F-statistic: 2371 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "GAS" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -544857 -371247 -243608 259857 3410851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1680527.8 4682.7 358.88 <0.0000000000000002 ***
## op_count 16768.7 241.8 69.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 513000 on 29998 degrees of freedom
## Multiple R-squared: 0.1382, Adjusted R-squared: 0.1381
## F-statistic: 4809 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPDEST" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -135366 -62163 -36586 19498 8466349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 271484.47 1142.13 237.7 <0.0000000000000002 ***
## op_count 7334.28 58.98 124.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 125100 on 29998 degrees of freedom
## Multiple R-squared: 0.3401, Adjusted R-squared: 0.3401
## F-statistic: 1.546e+04 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH1" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -486319 -333436 -226197 217285 8778953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1669213.4 4285.1 389.54 <0.0000000000000002 ***
## op_count 14408.3 221.3 65.11 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 469400 on 29998 degrees of freedom
## Multiple R-squared: 0.1238, Adjusted R-squared: 0.1238
## F-statistic: 4240 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH2" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -489229 -336228 -226043 212062 4186198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1671055.2 4347.6 384.37 <0.0000000000000002 ***
## op_count 14511.6 224.5 64.64 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476300 on 29998 degrees of freedom
## Multiple R-squared: 0.1222, Adjusted R-squared: 0.1222
## F-statistic: 4178 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH3" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -490551 -339062 -232877 207084 3947042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1672445.8 4435.8 377.03 <0.0000000000000002 ***
## op_count 14645.1 229.1 63.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 485900 on 29998 degrees of freedom
## Multiple R-squared: 0.1199, Adjusted R-squared: 0.1199
## F-statistic: 4088 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH4" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -491347 -337460 -227770 214512 5894733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1673567.3 4325.2 386.94 <0.0000000000000002 ***
## op_count 14413.9 223.4 64.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 473800 on 29998 degrees of freedom
## Multiple R-squared: 0.1219, Adjusted R-squared: 0.1219
## F-statistic: 4165 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH5" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -492410 -334672 -225769 215258 6244546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668602.2 4305.7 387.53 <0.0000000000000002 ***
## op_count 14602.5 222.3 65.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 471700 on 29998 degrees of freedom
## Multiple R-squared: 0.1257, Adjusted R-squared: 0.1257
## F-statistic: 4313 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH6" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -485549 -336377 -224588 214741 9719174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1670748.6 4316.1 387.10 <0.0000000000000002 ***
## op_count 14516.4 222.9 65.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 472800 on 29998 degrees of freedom
## Multiple R-squared: 0.1239, Adjusted R-squared: 0.1239
## F-statistic: 4242 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH7" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -486615 -333298 -223939 214198 3213745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1665110.9 4270.3 389.93 <0.0000000000000002 ***
## op_count 14672.2 220.5 66.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 467800 on 29998 degrees of freedom
## Multiple R-squared: 0.1286, Adjusted R-squared: 0.1286
## F-statistic: 4427 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -477797 -333077 -229906 215641 3964884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1670119.5 4275.7 390.61 <0.0000000000000002 ***
## op_count 14322.8 220.8 64.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 468400 on 29998 degrees of freedom
## Multiple R-squared: 0.123, Adjusted R-squared: 0.123
## F-statistic: 4208 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH9" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -493293 -336326 -226318 213690 3788431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668933 4338 384.73 <0.0000000000000002 ***
## op_count 14648 224 65.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 475200 on 29998 degrees of freedom
## Multiple R-squared: 0.1248, Adjusted R-squared: 0.1247
## F-statistic: 4276 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH10" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -487853 -330064 -225463 220750 3995086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1664820.1 4204.4 395.97 <0.0000000000000002 ***
## op_count 14499.0 217.1 66.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 460600 on 29998 degrees of freedom
## Multiple R-squared: 0.1294, Adjusted R-squared: 0.1294
## F-statistic: 4460 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH11" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -489353 -333141 -223275 214806 6853663
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668486.6 4278.1 390.00 <0.0000000000000002 ***
## op_count 14555.6 220.9 65.89 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 468600 on 29998 degrees of freedom
## Multiple R-squared: 0.1264, Adjusted R-squared: 0.1264
## F-statistic: 4341 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH12" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -484445 -334398 -229075 215912 5081080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1669972.6 4324.1 386.20 <0.0000000000000002 ***
## op_count 14426.7 223.3 64.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 473700 on 29998 degrees of freedom
## Multiple R-squared: 0.1222, Adjusted R-squared: 0.1221
## F-statistic: 4174 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH13" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -486441 -334095 -226119 215214 4839447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1666726.8 4290.0 388.52 <0.0000000000000002 ***
## op_count 14609.5 221.5 65.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 469900 on 29998 degrees of freedom
## Multiple R-squared: 0.1266, Adjusted R-squared: 0.1266
## F-statistic: 4349 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH14" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -493186 -337940 -231345 211986 12833420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1673542.7 4422.8 378.39 <0.0000000000000002 ***
## op_count 14401.6 228.4 63.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 484500 on 29998 degrees of freedom
## Multiple R-squared: 0.117, Adjusted R-squared: 0.117
## F-statistic: 3976 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH15" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -474266 -331060 -221471 218868 3132757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1665789.2 4207.2 395.9 <0.0000000000000002 ***
## op_count 14490.4 217.3 66.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 460900 on 29998 degrees of freedom
## Multiple R-squared: 0.1291, Adjusted R-squared: 0.1291
## F-statistic: 4448 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH16" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -478604 -329886 -223806 220815 12877749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1665844.2 4249.2 392.04 <0.0000000000000002 ***
## op_count 14323.2 219.4 65.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 465500 on 29998 degrees of freedom
## Multiple R-squared: 0.1244, Adjusted R-squared: 0.1243
## F-statistic: 4261 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH17" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -489168 -336430 -225000 213529 8699409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1666923.5 4372.7 381.21 <0.0000000000000002 ***
## op_count 14792.1 225.8 65.51 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 479000 on 29998 degrees of freedom
## Multiple R-squared: 0.1251, Adjusted R-squared: 0.1251
## F-statistic: 4291 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH18" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -496639 -333951 -227715 215893 5865073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668713 4299 388.16 <0.0000000000000002 ***
## op_count 14429 222 64.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 470900 on 29998 degrees of freedom
## Multiple R-squared: 0.1234, Adjusted R-squared: 0.1234
## F-statistic: 4224 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH19" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -486716 -332903 -225676 216119 8351796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668245.9 4285.5 389.27 <0.0000000000000002 ***
## op_count 14440.6 221.3 65.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 469500 on 29998 degrees of freedom
## Multiple R-squared: 0.1243, Adjusted R-squared: 0.1243
## F-statistic: 4258 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH20" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -490668 -334174 -228436 216451 4488982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668196.9 4287.3 389.10 <0.0000000000000002 ***
## op_count 14535.3 221.4 65.65 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 469700 on 29998 degrees of freedom
## Multiple R-squared: 0.1256, Adjusted R-squared: 0.1256
## F-statistic: 4310 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH21" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -480995 -338361 -231232 212574 5983020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1677845.1 4374.6 383.54 <0.0000000000000002 ***
## op_count 14174.2 225.9 62.74 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 479200 on 29998 degrees of freedom
## Multiple R-squared: 0.116, Adjusted R-squared: 0.116
## F-statistic: 3937 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH22" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -485385 -332610 -227755 213738 3566347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1663918.8 4307.9 386.25 <0.0000000000000002 ***
## op_count 14717.8 222.5 66.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 471900 on 29998 degrees of freedom
## Multiple R-squared: 0.1273, Adjusted R-squared: 0.1273
## F-statistic: 4377 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH23" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -492810 -339883 -230803 209382 16680952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1670897.0 4498.5 371.43 <0.0000000000000002 ***
## op_count 14776.5 232.3 63.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 492800 on 29998 degrees of freedom
## Multiple R-squared: 0.1188, Adjusted R-squared: 0.1188
## F-statistic: 4046 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH24" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -481627 -335313 -229977 214480 15524607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1670606.6 4401.6 379.55 <0.0000000000000002 ***
## op_count 14498.0 227.3 63.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 482200 on 29998 degrees of freedom
## Multiple R-squared: 0.1194, Adjusted R-squared: 0.1194
## F-statistic: 4068 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH25" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -481453 -334899 -225550 216819 15474330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1673690.4 4348.4 384.90 <0.0000000000000002 ***
## op_count 14179.5 224.5 63.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476300 on 29998 degrees of freedom
## Multiple R-squared: 0.1173, Adjusted R-squared: 0.1173
## F-statistic: 3987 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH26" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -492537 -338384 -231751 215402 3362459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1676569.7 4344.0 385.95 <0.0000000000000002 ***
## op_count 14325.8 224.3 63.86 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 475900 on 29998 degrees of freedom
## Multiple R-squared: 0.1197, Adjusted R-squared: 0.1197
## F-statistic: 4078 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH27" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -488814 -336840 -225554 212808 5811144
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668020.2 4354.1 383.1 <0.0000000000000002 ***
## op_count 14816.5 224.8 65.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 477000 on 29998 degrees of freedom
## Multiple R-squared: 0.1265, Adjusted R-squared: 0.1264
## F-statistic: 4342 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH28" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -482572 -331603 -223044 217642 3652932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1667441.8 4237.3 393.52 <0.0000000000000002 ***
## op_count 14454.2 218.8 66.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 464200 on 29998 degrees of freedom
## Multiple R-squared: 0.127, Adjusted R-squared: 0.127
## F-statistic: 4364 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH29" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -479960 -332906 -225689 220408 8653851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668583.5 4235.4 393.96 <0.0000000000000002 ***
## op_count 14484.7 218.7 66.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 464000 on 29998 degrees of freedom
## Multiple R-squared: 0.1276, Adjusted R-squared: 0.1275
## F-statistic: 4386 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH30" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -484838 -334535 -220252 216143 3634176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1668751 4260 391.74 <0.0000000000000002 ***
## op_count 14495 220 65.89 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 466600 on 29998 degrees of freedom
## Multiple R-squared: 0.1264, Adjusted R-squared: 0.1264
## F-statistic: 4342 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH31" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -486562 -334036 -226309 214903 5398817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1669356.6 4268.8 391.06 <0.0000000000000002 ***
## op_count 14585.4 220.4 66.17 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 467600 on 29998 degrees of freedom
## Multiple R-squared: 0.1274, Adjusted R-squared: 0.1273
## F-statistic: 4378 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH32" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -481988 -331403 -223603 208787 4045478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1663851.3 4263.8 390.23 <0.0000000000000002 ***
## op_count 15042.7 220.2 68.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 467100 on 29998 degrees of freedom
## Multiple R-squared: 0.1346, Adjusted R-squared: 0.1346
## F-statistic: 4668 on 1 and 29998 DF, p-value: < 0.00000000000000022
##
## [1] "DUP1" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -825565 -712726 -666007 799691 6833475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4130681.7 14984.7 275.659 <0.0000000000000002 ***
## op_count 9688.8 504.0 19.223 <0.0000000000000002 ***
## arg0 -322.5 670.0 -0.481 0.63
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1069000 on 29997 degrees of freedom
## Multiple R-squared: 0.01218, Adjusted R-squared: 0.01211
## F-statistic: 184.9 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP2" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -834008 -709853 -665155 803508 14769119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4128950.0 14127.0 292.273 <0.0000000000000002 ***
## op_count 10052.7 500.3 20.092 <0.0000000000000002 ***
## arg0 -603.1 635.6 -0.949 0.343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1061000 on 29997 degrees of freedom
## Multiple R-squared: 0.01331, Adjusted R-squared: 0.01324
## F-statistic: 202.3 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP3" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -836980 -715944 -670104 791729 6626789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4133204.3 15027.4 275.045 <0.0000000000000002 ***
## op_count 9831.8 511.6 19.216 <0.0000000000000002 ***
## arg0 -371.9 702.9 -0.529 0.597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1085000 on 29997 degrees of freedom
## Multiple R-squared: 0.01217, Adjusted R-squared: 0.0121
## F-statistic: 184.8 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP4" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -838664 -721166 -674241 791295 9671516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4139741.9 14901.1 277.814 <0.0000000000000002 ***
## op_count 9454.3 513.9 18.397 <0.0000000000000002 ***
## arg0 -135.5 673.2 -0.201 0.84
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1090000 on 29997 degrees of freedom
## Multiple R-squared: 0.01116, Adjusted R-squared: 0.01109
## F-statistic: 169.2 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP5" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -829995 -709774 -663126 803450 6704135
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4133025.9 14372.8 287.559 <0.0000000000000002 ***
## op_count 9778.5 499.9 19.562 <0.0000000000000002 ***
## arg0 -653.3 637.3 -1.025 0.305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1060000 on 29997 degrees of freedom
## Multiple R-squared: 0.01263, Adjusted R-squared: 0.01256
## F-statistic: 191.9 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP6" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -830299 -716662 -670303 798294 6697353
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4123224.517 15419.535 267.403 <0.0000000000000002 ***
## op_count 9911.067 506.761 19.558 <0.0000000000000002 ***
## arg0 -9.266 696.963 -0.013 0.989
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075000 on 29997 degrees of freedom
## Multiple R-squared: 0.01259, Adjusted R-squared: 0.01252
## F-statistic: 191.3 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP7" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -837982 -719852 -672416 804249 6875743
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4159433.69 14972.33 277.808 <0.0000000000000002 ***
## op_count 9576.08 503.28 19.027 <0.0000000000000002 ***
## arg0 -58.49 693.05 -0.084 0.933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1068000 on 29997 degrees of freedom
## Multiple R-squared: 0.01193, Adjusted R-squared: 0.01186
## F-statistic: 181 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -856990 -734178 -686482 801284 7207796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4177490.8 14688.5 284.40 <0.0000000000000002 ***
## op_count 10462.5 516.7 20.25 <0.0000000000000002 ***
## arg0 298.7 710.5 0.42 0.674
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1096000 on 29997 degrees of freedom
## Multiple R-squared: 0.01349, Adjusted R-squared: 0.01343
## F-statistic: 205.1 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP9" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -848533 -720388 -674451 794980 13099809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4123184.8 15109.1 272.89 <0.0000000000000002 ***
## op_count 10055.2 513.2 19.59 <0.0000000000000002 ***
## arg0 269.1 708.6 0.38 0.704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1089000 on 29997 degrees of freedom
## Multiple R-squared: 0.01264, Adjusted R-squared: 0.01257
## F-statistic: 192 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP10" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -825182 -714676 -668533 796119 6301492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4115030.7 15084.2 272.805 <0.0000000000000002 ***
## op_count 9994.0 506.8 19.721 <0.0000000000000002 ***
## arg0 514.9 649.8 0.793 0.428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075000 on 29997 degrees of freedom
## Multiple R-squared: 0.01282, Adjusted R-squared: 0.01275
## F-statistic: 194.8 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP11" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -867041 -744138 -692709 810204 6845046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4231377.9 15352.5 275.615 <0.0000000000000002 ***
## op_count 10258.4 513.0 19.996 <0.0000000000000002 ***
## arg0 -113.2 695.0 -0.163 0.871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1088000 on 29997 degrees of freedom
## Multiple R-squared: 0.01315, Adjusted R-squared: 0.01309
## F-statistic: 199.9 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP12" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -840531 -721679 -674903 791132 16949621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4133274.3 14753.9 280.15 <0.0000000000000002 ***
## op_count 9529.4 514.9 18.51 <0.0000000000000002 ***
## arg0 287.0 666.9 0.43 0.667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1092000 on 29997 degrees of freedom
## Multiple R-squared: 0.0113, Adjusted R-squared: 0.01123
## F-statistic: 171.3 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP13" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -839295 -719532 -672854 803733 11139342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4154035.3 15118.2 274.77 <0.0000000000000002 ***
## op_count 9518.3 499.7 19.05 <0.0000000000000002 ***
## arg0 242.5 673.5 0.36 0.719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1060000 on 29997 degrees of freedom
## Multiple R-squared: 0.01195, Adjusted R-squared: 0.01189
## F-statistic: 181.5 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP14" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -844853 -722887 -675862 803083 16300930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4151564.5 14797.4 280.561 <0.0000000000000002 ***
## op_count 9899.6 508.6 19.463 <0.0000000000000002 ***
## arg0 133.8 682.9 0.196 0.845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1079000 on 29997 degrees of freedom
## Multiple R-squared: 0.01247, Adjusted R-squared: 0.01241
## F-statistic: 189.4 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP15" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -835188 -713275 -666903 799044 9016078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4123968.1 15880.3 259.690 <0.0000000000000002 ***
## op_count 10043.3 505.7 19.861 <0.0000000000000002 ***
## arg0 -109.5 721.8 -0.152 0.879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1073000 on 29997 degrees of freedom
## Multiple R-squared: 0.01298, Adjusted R-squared: 0.01291
## F-statistic: 197.2 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "DUP16" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -907165 -770665 -712961 813818 12491745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4318574.4 15729.2 274.557 <0.0000000000000002 ***
## op_count 10540.6 532.2 19.805 <0.0000000000000002 ***
## arg0 -344.4 707.5 -0.487 0.626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1129000 on 29997 degrees of freedom
## Multiple R-squared: 0.01291, Adjusted R-squared: 0.01285
## F-statistic: 196.2 on 2 and 29997 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP1" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1009182 -567636 -466119 613531 9825542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2332414.6 14478.1 161.100 < 0.0000000000000002 ***
## op_count 25701.6 394.7 65.114 < 0.0000000000000002 ***
## arg0 2581.4 528.7 4.882 0.00000105 ***
## arg1 846.0 514.8 1.644 0.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 837300 on 29996 degrees of freedom
## Multiple R-squared: 0.1245, Adjusted R-squared: 0.1244
## F-statistic: 1422 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP2" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1019310 -579098 -476664 604516 9125621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2345771.2 15122.7 155.116 < 0.0000000000000002 ***
## op_count 25594.2 405.7 63.080 < 0.0000000000000002 ***
## arg0 595.5 564.1 1.056 0.291
## arg1 2611.8 533.8 4.893 0.000000997 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 860700 on 29996 degrees of freedom
## Multiple R-squared: 0.1178, Adjusted R-squared: 0.1177
## F-statistic: 1335 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP3" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -974557 -569853 -477335 604781 7326450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2337326.8 14359.9 162.768 < 0.0000000000000002 ***
## op_count 26373.0 397.7 66.309 < 0.0000000000000002 ***
## arg0 1657.4 524.5 3.160 0.00158 **
## arg1 668.2 511.6 1.306 0.19149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 843700 on 29996 degrees of freedom
## Multiple R-squared: 0.1281, Adjusted R-squared: 0.128
## F-statistic: 1469 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP4" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1001165 -579501 -481493 598220 6618157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2418452.69 15000.79 161.222 <0.0000000000000002 ***
## op_count 25936.84 405.18 64.013 <0.0000000000000002 ***
## arg0 -78.19 560.58 -0.139 0.889
## arg1 -1244.96 535.07 -2.327 0.020 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 859500 on 29996 degrees of freedom
## Multiple R-squared: 0.1203, Adjusted R-squared: 0.1202
## F-statistic: 1368 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP5" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -997432 -572869 -482522 608023 13516765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2401706.84 15578.99 154.163 <0.0000000000000002 ***
## op_count 25882.88 402.96 64.232 <0.0000000000000002 ***
## arg0 28.68 534.06 0.054 0.957
## arg1 -439.15 574.54 -0.764 0.445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 854800 on 29996 degrees of freedom
## Multiple R-squared: 0.1209, Adjusted R-squared: 0.1208
## F-statistic: 1375 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP6" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1002151 -577438 -487761 617588 7810339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2433052.74 15651.93 155.447 <0.0000000000000002 ***
## op_count 25882.27 407.39 63.531 <0.0000000000000002 ***
## arg0 -1094.67 547.93 -1.998 0.0457 *
## arg1 41.46 541.28 0.077 0.9389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 864200 on 29996 degrees of freedom
## Multiple R-squared: 0.1187, Adjusted R-squared: 0.1186
## F-statistic: 1347 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP7" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1011746 -585524 -492430 631587 7025593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2456391.52 15240.01 161.180 <0.0000000000000002 ***
## op_count 25555.38 407.17 62.764 <0.0000000000000002 ***
## arg0 -258.27 564.53 -0.457 0.647
## arg1 -38.88 580.54 -0.067 0.947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 863700 on 29996 degrees of freedom
## Multiple R-squared: 0.1161, Adjusted R-squared: 0.116
## F-statistic: 1313 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP8" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -986413 -567670 -474878 608098 5449407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2383181.17 14200.63 167.822 <0.0000000000000002 ***
## op_count 26126.48 400.00 65.316 <0.0000000000000002 ***
## arg0 -34.63 530.87 -0.065 0.948
## arg1 -49.52 542.14 -0.091 0.927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 848500 on 29996 degrees of freedom
## Multiple R-squared: 0.1245, Adjusted R-squared: 0.1244
## F-statistic: 1422 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP9" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1003402 -578806 -487753 596250 15165622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2429339.2 16155.4 150.373 <0.0000000000000002 ***
## op_count 25956.0 411.2 63.129 <0.0000000000000002 ***
## arg0 -211.2 580.2 -0.364 0.7159
## arg1 -1229.2 551.5 -2.229 0.0258 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 872200 on 29996 degrees of freedom
## Multiple R-squared: 0.1174, Adjusted R-squared: 0.1173
## F-statistic: 1330 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP10" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1027478 -592883 -504995 638784 5225823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2476036.2 15932.3 155.410 <0.0000000000000002 ***
## op_count 26931.5 413.5 65.136 <0.0000000000000002 ***
## arg0 407.0 535.3 0.760 0.447
## arg1 -1072.6 585.6 -1.832 0.067 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 877100 on 29996 degrees of freedom
## Multiple R-squared: 0.124, Adjusted R-squared: 0.1239
## F-statistic: 1416 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP11" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1026664 -574574 -471696 603114 5348366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2396253.6 14372.8 166.722 <0.0000000000000002 ***
## op_count 25281.6 399.2 63.326 <0.0000000000000002 ***
## arg0 895.6 546.9 1.638 0.102
## arg1 -309.1 542.3 -0.570 0.569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 846900 on 29996 degrees of freedom
## Multiple R-squared: 0.118, Adjusted R-squared: 0.1179
## F-statistic: 1338 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP12" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -996408 -581578 -496351 613501 11066225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2395428.8 14310.5 167.390 <0.0000000000000002 ***
## op_count 26754.2 411.0 65.101 <0.0000000000000002 ***
## arg0 228.9 531.5 0.431 0.667
## arg1 409.3 543.4 0.753 0.451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 871800 on 29996 degrees of freedom
## Multiple R-squared: 0.1238, Adjusted R-squared: 0.1237
## F-statistic: 1413 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP13" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -991913 -576219 -491994 614044 5055159
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2389699.03 14237.58 167.844 <0.0000000000000002 ***
## op_count 26687.91 405.70 65.782 <0.0000000000000002 ***
## arg0 51.19 540.40 0.095 0.925
## arg1 675.05 533.02 1.266 0.205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 860600 on 29996 degrees of freedom
## Multiple R-squared: 0.1261, Adjusted R-squared: 0.126
## F-statistic: 1443 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP14" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -993584 -582117 -481306 596251 5449904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2384059.7 14592.2 163.379 <0.0000000000000002 ***
## op_count 25562.5 407.5 62.736 <0.0000000000000002 ***
## arg0 1038.7 570.4 1.821 0.0686 .
## arg1 461.8 542.8 0.851 0.3949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 864400 on 29996 degrees of freedom
## Multiple R-squared: 0.1161, Adjusted R-squared: 0.116
## F-statistic: 1313 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP15" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1047306 -602174 -499574 663689 7377915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2543547.3 15898.4 159.987 <0.0000000000000002 ***
## op_count 27081.0 422.4 64.114 <0.0000000000000002 ***
## arg0 481.7 562.8 0.856 0.3921
## arg1 -1299.8 562.7 -2.310 0.0209 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 896000 on 29996 degrees of freedom
## Multiple R-squared: 0.1207, Adjusted R-squared: 0.1206
## F-statistic: 1372 on 3 and 29996 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP16" "besu"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1040086 -606805 -501191 639569 12333241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2517683.15 15788.52 159.463 <0.0000000000000002 ***
## op_count 25034.85 428.26 58.457 <0.0000000000000002 ***
## arg0 46.51 547.66 0.085 0.932
## arg1 -191.89 590.75 -0.325 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 908500 on 29996 degrees of freedom
## Multiple R-squared: 0.1023, Adjusted R-squared: 0.1022
## F-statistic: 1139 on 3 and 29996 DF, p-value: < 0.00000000000000022estimates## opcode env has_significant has_impacting estimate_marginal_ns
## 1 ADD besu FALSE FALSE 59139.7381507484
## 2 MUL besu FALSE FALSE 90509.6163333146
## 3 SUB besu FALSE FALSE 57096.6717899886
## 4 DIV besu TRUE TRUE 77384.6505280846
## 5 SDIV besu TRUE TRUE 133662.805810687
## 6 MOD besu TRUE TRUE 130951.170034169
## 7 SMOD besu TRUE TRUE 132826.891880777
## 8 ADDMOD besu TRUE TRUE 160454.16620875
## 9 MULMOD besu TRUE TRUE 174246.265402108
## 10 EXP besu TRUE TRUE 343496.465743394
## 11 SIGNEXTEND besu FALSE FALSE 145660.886003304
## 12 LT besu FALSE FALSE 52897.4939866554
## 13 GT besu FALSE FALSE 53246.989626656
## 14 SLT besu FALSE FALSE 67747.9178866525
## 15 SGT besu FALSE FALSE 66898.8960433187
## 16 EQ besu FALSE FALSE 51899.3274633225
## 17 ISZERO besu FALSE FALSE 31369.0441799936
## 18 AND besu FALSE FALSE 55993.4300233219
## 19 OR besu FALSE FALSE 55139.0193233221
## 20 XOR besu FALSE FALSE 55741.3444499885
## 21 NOT besu FALSE FALSE 34886.7075633263
## 22 BYTE besu FALSE FALSE 52101.6437999896
## 23 SHL besu FALSE FALSE 22002.1803233289
## 24 SHR besu FALSE FALSE 23305.8617833287
## 25 SAR besu FALSE FALSE 22042.9542299955
## 26 ADDRESS besu FALSE FALSE 9322.91216666482
## 27 ORIGIN besu FALSE FALSE 9405.74188666476
## 28 CALLER besu FALSE FALSE 9000.24704666464
## 29 CALLVALUE besu FALSE FALSE 9593.64332666484
## 30 CALLDATALOAD besu FALSE FALSE 59845.7824299863
## 31 CALLDATASIZE besu FALSE FALSE 16991.9485599965
## 32 CALLDATACOPY besu TRUE TRUE -3029.32388348525
## 33 CODESIZE besu FALSE FALSE 17442.692686663
## 34 CODECOPY besu TRUE TRUE -1692.22649013348
## 35 GASPRICE besu FALSE FALSE 9332.23963333153
## 36 RETURNDATASIZE besu FALSE FALSE 9721.5986399978
## 37 RETURNDATACOPY besu TRUE TRUE 10941.6029209696
## 38 COINBASE besu FALSE FALSE 9094.41983999815
## 39 TIMESTAMP besu FALSE FALSE 16775.7228166631
## 40 NUMBER besu FALSE FALSE 16343.5492299966
## 41 DIFFICULTY besu FALSE FALSE 9616.64865333125
## 42 GASLIMIT besu FALSE FALSE 16541.8412599966
## 43 CHAINID besu FALSE FALSE 9332.85972999808
## 44 SELFBALANCE besu FALSE FALSE 438728.340139909
## 45 POP besu FALSE FALSE 21192.6735233291
## 46 MLOAD besu FALSE FALSE 24847.6350899939
## 47 MSTORE besu FALSE FALSE 33765.0043666591
## 48 MSTORE8 besu FALSE FALSE 24950.9338566606
## 49 JUMP besu FALSE FALSE 15935.0661899967
## 50 JUMPI besu FALSE FALSE 19058.4796699961
## 51 PC besu FALSE FALSE 16794.2684833298
## 52 MSIZE besu FALSE FALSE 9665.52925999811
## 53 GAS besu FALSE FALSE 16768.6736299966
## 54 JUMPDEST besu FALSE FALSE 7334.28496333177
## 55 PUSH1 besu FALSE FALSE 14408.3057799971
## 56 PUSH2 besu FALSE FALSE 14511.5522299971
## 57 PUSH3 besu FALSE FALSE 14645.1030233302
## 58 PUSH4 besu FALSE FALSE 14413.949889997
## 59 PUSH5 besu FALSE FALSE 14602.4568699969
## 60 PUSH6 besu FALSE FALSE 14516.4491499969
## 61 PUSH7 besu FALSE FALSE 14672.180239997
## 62 PUSH8 besu FALSE FALSE 14322.7703966636
## 63 PUSH9 besu FALSE FALSE 14648.0053666637
## 64 PUSH10 besu FALSE FALSE 14499.0223766635
## 65 PUSH11 besu FALSE FALSE 14555.6389199972
## 66 PUSH12 besu FALSE FALSE 14426.6753233303
## 67 PUSH13 besu FALSE FALSE 14609.5315333301
## 68 PUSH14 besu FALSE FALSE 14401.610899997
## 69 PUSH15 besu FALSE FALSE 14490.434309997
## 70 PUSH16 besu FALSE FALSE 14323.1997133303
## 71 PUSH17 besu FALSE FALSE 14792.1068333304
## 72 PUSH18 besu FALSE FALSE 14428.7569233302
## 73 PUSH19 besu FALSE FALSE 14440.603539997
## 74 PUSH20 besu FALSE FALSE 14535.2961233302
## 75 PUSH21 besu FALSE FALSE 14174.2461266638
## 76 PUSH22 besu FALSE FALSE 14717.7917033303
## 77 PUSH23 besu FALSE FALSE 14776.456559997
## 78 PUSH24 besu FALSE FALSE 14498.0350066634
## 79 PUSH25 besu FALSE FALSE 14179.5140766638
## 80 PUSH26 besu FALSE FALSE 14325.8441666638
## 81 PUSH27 besu FALSE FALSE 14816.5133666637
## 82 PUSH28 besu FALSE FALSE 14454.176679997
## 83 PUSH29 besu FALSE FALSE 14484.7470999972
## 84 PUSH30 besu FALSE FALSE 14495.0773699971
## 85 PUSH31 besu FALSE FALSE 14585.4081999971
## 86 PUSH32 besu FALSE FALSE 15042.6825099967
## 87 DUP1 besu FALSE FALSE 9688.77807666469
## 88 DUP2 besu FALSE FALSE 10052.7201833313
## 89 DUP3 besu FALSE FALSE 9831.77762999759
## 90 DUP4 besu FALSE FALSE 9454.27888333103
## 91 DUP5 besu FALSE FALSE 9778.45543333141
## 92 DUP6 besu FALSE FALSE 9911.06687999801
## 93 DUP7 besu FALSE FALSE 9576.07521666475
## 94 DUP8 besu FALSE FALSE 10462.4805033313
## 95 DUP9 besu FALSE FALSE 10055.2243466646
## 96 DUP10 besu FALSE FALSE 9993.95662999795
## 97 DUP11 besu FALSE FALSE 10258.419089998
## 98 DUP12 besu FALSE FALSE 9529.44386666478
## 99 DUP13 besu FALSE FALSE 9518.28127999802
## 100 DUP14 besu FALSE FALSE 9899.60800666429
## 101 DUP15 besu FALSE FALSE 10043.2925666646
## 102 DUP16 besu FALSE FALSE 10540.5990233311
## 103 SWAP1 besu FALSE FALSE 25701.5755133282
## 104 SWAP2 besu FALSE FALSE 25594.2300233282
## 105 SWAP3 besu FALSE FALSE 26372.9897199944
## 106 SWAP4 besu FALSE FALSE 25936.8432466615
## 107 SWAP5 besu FALSE FALSE 25882.8786066616
## 108 SWAP6 besu FALSE FALSE 25882.2739866615
## 109 SWAP7 besu FALSE FALSE 25555.3819333282
## 110 SWAP8 besu FALSE FALSE 26126.4772833284
## 111 SWAP9 besu FALSE FALSE 25955.9735666614
## 112 SWAP10 besu FALSE FALSE 26931.4635999946
## 113 SWAP11 besu FALSE FALSE 25281.5860966614
## 114 SWAP12 besu FALSE FALSE 26754.1942333279
## 115 SWAP13 besu FALSE FALSE 26687.9064099944
## 116 SWAP14 besu FALSE FALSE 25562.452349995
## 117 SWAP15 besu FALSE FALSE 27080.950883328
## 118 SWAP16 besu FALSE FALSE 25034.8517999951
## arg0_ns arg1_ns arg2_ns expensive_ns arg0_ns_stderr
## 1 <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> 53490.4167445755 <NA>
## 5 <NA> <NA> <NA> 61718.8876260387 <NA>
## 6 <NA> <NA> <NA> 68190.6096058811 <NA>
## 7 <NA> <NA> <NA> 66527.0478967747 <NA>
## 8 <NA> <NA> <NA> 105343.306489307 <NA>
## 9 <NA> <NA> <NA> 127018.823017713 <NA>
## 10 <NA> 163574.957097498 <NA> <NA> <NA>
## 11 <NA> <NA> <NA> <NA> <NA>
## 12 <NA> <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> <NA> <NA> <NA>
## 15 <NA> <NA> <NA> <NA> <NA>
## 16 <NA> <NA> <NA> <NA> <NA>
## 17 <NA> <NA> <NA> <NA> <NA>
## 18 <NA> <NA> <NA> <NA> <NA>
## 19 <NA> <NA> <NA> <NA> <NA>
## 20 <NA> <NA> <NA> <NA> <NA>
## 21 <NA> <NA> <NA> <NA> <NA>
## 22 <NA> <NA> <NA> <NA> <NA>
## 23 <NA> <NA> <NA> <NA> <NA>
## 24 <NA> <NA> <NA> <NA> <NA>
## 25 <NA> <NA> <NA> <NA> <NA>
## 26 <NA> <NA> <NA> <NA> <NA>
## 27 <NA> <NA> <NA> <NA> <NA>
## 28 <NA> <NA> <NA> <NA> <NA>
## 29 <NA> <NA> <NA> <NA> <NA>
## 30 <NA> <NA> <NA> <NA> <NA>
## 31 <NA> <NA> <NA> <NA> <NA>
## 32 <NA> <NA> 138.105491370495 <NA> <NA>
## 33 <NA> <NA> <NA> <NA> <NA>
## 34 <NA> <NA> 138.023458776764 <NA> <NA>
## 35 <NA> <NA> <NA> <NA> <NA>
## 36 <NA> <NA> <NA> <NA> <NA>
## 37 <NA> <NA> 137.080651305847 <NA> <NA>
## 38 <NA> <NA> <NA> <NA> <NA>
## 39 <NA> <NA> <NA> <NA> <NA>
## 40 <NA> <NA> <NA> <NA> <NA>
## 41 <NA> <NA> <NA> <NA> <NA>
## 42 <NA> <NA> <NA> <NA> <NA>
## 43 <NA> <NA> <NA> <NA> <NA>
## 44 <NA> <NA> <NA> <NA> <NA>
## 45 <NA> <NA> <NA> <NA> <NA>
## 46 <NA> <NA> <NA> <NA> <NA>
## 47 <NA> <NA> <NA> <NA> <NA>
## 48 <NA> <NA> <NA> <NA> <NA>
## 49 <NA> <NA> <NA> <NA> <NA>
## 50 <NA> <NA> <NA> <NA> <NA>
## 51 <NA> <NA> <NA> <NA> <NA>
## 52 <NA> <NA> <NA> <NA> <NA>
## 53 <NA> <NA> <NA> <NA> <NA>
## 54 <NA> <NA> <NA> <NA> <NA>
## 55 <NA> <NA> <NA> <NA> <NA>
## 56 <NA> <NA> <NA> <NA> <NA>
## 57 <NA> <NA> <NA> <NA> <NA>
## 58 <NA> <NA> <NA> <NA> <NA>
## 59 <NA> <NA> <NA> <NA> <NA>
## 60 <NA> <NA> <NA> <NA> <NA>
## 61 <NA> <NA> <NA> <NA> <NA>
## 62 <NA> <NA> <NA> <NA> <NA>
## 63 <NA> <NA> <NA> <NA> <NA>
## 64 <NA> <NA> <NA> <NA> <NA>
## 65 <NA> <NA> <NA> <NA> <NA>
## 66 <NA> <NA> <NA> <NA> <NA>
## 67 <NA> <NA> <NA> <NA> <NA>
## 68 <NA> <NA> <NA> <NA> <NA>
## 69 <NA> <NA> <NA> <NA> <NA>
## 70 <NA> <NA> <NA> <NA> <NA>
## 71 <NA> <NA> <NA> <NA> <NA>
## 72 <NA> <NA> <NA> <NA> <NA>
## 73 <NA> <NA> <NA> <NA> <NA>
## 74 <NA> <NA> <NA> <NA> <NA>
## 75 <NA> <NA> <NA> <NA> <NA>
## 76 <NA> <NA> <NA> <NA> <NA>
## 77 <NA> <NA> <NA> <NA> <NA>
## 78 <NA> <NA> <NA> <NA> <NA>
## 79 <NA> <NA> <NA> <NA> <NA>
## 80 <NA> <NA> <NA> <NA> <NA>
## 81 <NA> <NA> <NA> <NA> <NA>
## 82 <NA> <NA> <NA> <NA> <NA>
## 83 <NA> <NA> <NA> <NA> <NA>
## 84 <NA> <NA> <NA> <NA> <NA>
## 85 <NA> <NA> <NA> <NA> <NA>
## 86 <NA> <NA> <NA> <NA> <NA>
## 87 <NA> <NA> <NA> <NA> <NA>
## 88 <NA> <NA> <NA> <NA> <NA>
## 89 <NA> <NA> <NA> <NA> <NA>
## 90 <NA> <NA> <NA> <NA> <NA>
## 91 <NA> <NA> <NA> <NA> <NA>
## 92 <NA> <NA> <NA> <NA> <NA>
## 93 <NA> <NA> <NA> <NA> <NA>
## 94 <NA> <NA> <NA> <NA> <NA>
## 95 <NA> <NA> <NA> <NA> <NA>
## 96 <NA> <NA> <NA> <NA> <NA>
## 97 <NA> <NA> <NA> <NA> <NA>
## 98 <NA> <NA> <NA> <NA> <NA>
## 99 <NA> <NA> <NA> <NA> <NA>
## 100 <NA> <NA> <NA> <NA> <NA>
## 101 <NA> <NA> <NA> <NA> <NA>
## 102 <NA> <NA> <NA> <NA> <NA>
## 103 <NA> <NA> <NA> <NA> <NA>
## 104 <NA> <NA> <NA> <NA> <NA>
## 105 <NA> <NA> <NA> <NA> <NA>
## 106 <NA> <NA> <NA> <NA> <NA>
## 107 <NA> <NA> <NA> <NA> <NA>
## 108 <NA> <NA> <NA> <NA> <NA>
## 109 <NA> <NA> <NA> <NA> <NA>
## 110 <NA> <NA> <NA> <NA> <NA>
## 111 <NA> <NA> <NA> <NA> <NA>
## 112 <NA> <NA> <NA> <NA> <NA>
## 113 <NA> <NA> <NA> <NA> <NA>
## 114 <NA> <NA> <NA> <NA> <NA>
## 115 <NA> <NA> <NA> <NA> <NA>
## 116 <NA> <NA> <NA> <NA> <NA>
## 117 <NA> <NA> <NA> <NA> <NA>
## 118 <NA> <NA> <NA> <NA> <NA>
## arg1_ns_stderr arg2_ns_stderr expensive_ns_stderr
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## 3 <NA> <NA> <NA>
## 4 <NA> <NA> 1276.72900980354
## 5 <NA> <NA> 1716.26250572823
## 6 <NA> <NA> 1714.00642195101
## 7 <NA> <NA> 1696.06287561235
## 8 <NA> <NA> 1942.88682692474
## 9 <NA> <NA> 2308.24647927741
## 10 2951.18955494159 <NA> <NA>
## 11 <NA> <NA> <NA>
## 12 <NA> <NA> <NA>
## 13 <NA> <NA> <NA>
## 14 <NA> <NA> <NA>
## 15 <NA> <NA> <NA>
## 16 <NA> <NA> <NA>
## 17 <NA> <NA> <NA>
## 18 <NA> <NA> <NA>
## 19 <NA> <NA> <NA>
## 20 <NA> <NA> <NA>
## 21 <NA> <NA> <NA>
## 22 <NA> <NA> <NA>
## 23 <NA> <NA> <NA>
## 24 <NA> <NA> <NA>
## 25 <NA> <NA> <NA>
## 26 <NA> <NA> <NA>
## 27 <NA> <NA> <NA>
## 28 <NA> <NA> <NA>
## 29 <NA> <NA> <NA>
## 30 <NA> <NA> <NA>
## 31 <NA> <NA> <NA>
## 32 <NA> 0.538725014664064 <NA>
## 33 <NA> <NA> <NA>
## 34 <NA> 0.591992983739053 <NA>
## 35 <NA> <NA> <NA>
## 36 <NA> <NA> <NA>
## 37 <NA> 0.609056715344308 <NA>
## 38 <NA> <NA> <NA>
## 39 <NA> <NA> <NA>
## 40 <NA> <NA> <NA>
## 41 <NA> <NA> <NA>
## 42 <NA> <NA> <NA>
## 43 <NA> <NA> <NA>
## 44 <NA> <NA> <NA>
## 45 <NA> <NA> <NA>
## 46 <NA> <NA> <NA>
## 47 <NA> <NA> <NA>
## 48 <NA> <NA> <NA>
## 49 <NA> <NA> <NA>
## 50 <NA> <NA> <NA>
## 51 <NA> <NA> <NA>
## 52 <NA> <NA> <NA>
## 53 <NA> <NA> <NA>
## 54 <NA> <NA> <NA>
## 55 <NA> <NA> <NA>
## 56 <NA> <NA> <NA>
## 57 <NA> <NA> <NA>
## 58 <NA> <NA> <NA>
## 59 <NA> <NA> <NA>
## 60 <NA> <NA> <NA>
## 61 <NA> <NA> <NA>
## 62 <NA> <NA> <NA>
## 63 <NA> <NA> <NA>
## 64 <NA> <NA> <NA>
## 65 <NA> <NA> <NA>
## 66 <NA> <NA> <NA>
## 67 <NA> <NA> <NA>
## 68 <NA> <NA> <NA>
## 69 <NA> <NA> <NA>
## 70 <NA> <NA> <NA>
## 71 <NA> <NA> <NA>
## 72 <NA> <NA> <NA>
## 73 <NA> <NA> <NA>
## 74 <NA> <NA> <NA>
## 75 <NA> <NA> <NA>
## 76 <NA> <NA> <NA>
## 77 <NA> <NA> <NA>
## 78 <NA> <NA> <NA>
## 79 <NA> <NA> <NA>
## 80 <NA> <NA> <NA>
## 81 <NA> <NA> <NA>
## 82 <NA> <NA> <NA>
## 83 <NA> <NA> <NA>
## 84 <NA> <NA> <NA>
## 85 <NA> <NA> <NA>
## 86 <NA> <NA> <NA>
## 87 <NA> <NA> <NA>
## 88 <NA> <NA> <NA>
## 89 <NA> <NA> <NA>
## 90 <NA> <NA> <NA>
## 91 <NA> <NA> <NA>
## 92 <NA> <NA> <NA>
## 93 <NA> <NA> <NA>
## 94 <NA> <NA> <NA>
## 95 <NA> <NA> <NA>
## 96 <NA> <NA> <NA>
## 97 <NA> <NA> <NA>
## 98 <NA> <NA> <NA>
## 99 <NA> <NA> <NA>
## 100 <NA> <NA> <NA>
## 101 <NA> <NA> <NA>
## 102 <NA> <NA> <NA>
## 103 <NA> <NA> <NA>
## 104 <NA> <NA> <NA>
## 105 <NA> <NA> <NA>
## 106 <NA> <NA> <NA>
## 107 <NA> <NA> <NA>
## 108 <NA> <NA> <NA>
## 109 <NA> <NA> <NA>
## 110 <NA> <NA> <NA>
## 111 <NA> <NA> <NA>
## 112 <NA> <NA> <NA>
## 113 <NA> <NA> <NA>
## 114 <NA> <NA> <NA>
## 115 <NA> <NA> <NA>
## 116 <NA> <NA> <NA>
## 117 <NA> <NA> <NA>
## 118 <NA> <NA> <NA>write.csv(estimates, paste0("../../local/", env, "_argument_estimated_cost.csv"), quote=FALSE, row.names=FALSE)